dm³ Nutrient Prediction Registry — Mechanistic Hypotheses for Clinical Nutrition Research

The dm³ framework specifies not only what each nutrient does, but how classes of nutrients relate to one another through the operator chain G = U ∘ F ∘ K ∘ C. Because the K operator classifies nutrients by spectroscopic stratum and the F operator governs the irreversible absorption fold, the framework generates structural predictions about nutrient interactions — timing effects, competitive absorption, synergistic strata, threshold doses — that are independent of the existing nutrition literature and can therefore be tested as genuinely novel hypotheses.

The eight predictions below are derived mechanistically. Each carries a dm³ derivation, a falsifiable hypothesis, a minimum viable study design (achievable with standard clinical resources: serum assays, dietary recalls, crossover RCT design), and the expected outcome under both confirmation and refutation. Target journals are suggested by impact factor and scope fit.

Prediction Overview

#	Prediction	dm³ basis	Novelty	Min. study size	Target journal
P-1	Post-perturbation timing of anthocyanins	μ-rung Lyapunov reset	Novel	n = 24 crossover RCT	AJCN / Nutrients
P-2	Chlorophyll → B12 tetrapyrrole scaffold in vegans	K-class porphyrin identity	Novel	n = 30 crossover RCT	EJCN / Am J Clin Nutr
P-3	Same-stratum carotenoid competition	K-eigenstate exclusion	Partially known	n = 20 crossover RCT	J Nutr / Nutrients
P-4	Spectral complementarity superadditivity	Contact surjectivity (Theorem N.2)	Novel	In vitro → n = 30 RCT	Food Chem / Nutrients
P-5	ε₀ = 1/3 nutrient threshold dose	Stability radius of dm³ attractor	Novel	n = 40 dose-response RCT	AJCN / Br J Nutr
P-6	F-operator timing window for fat-soluble nutrients	Whitney fold — bile salt micelle kinetics	Partially known	n = 18 crossover RCT	J Nutr / Lipids Health Dis
P-7	π-rung melatonin potentiates morning nutrient absorption	Reeb field periodicity T* = 2π	Novel	n = 24 crossover RCT	Chronobiol Int / Nutrients
P-8	Adjacent-stratum polyphenol + carotenoid synergy	n-bonacci ladder adjacency	Novel	n = 36 factorial RCT	Free Radic Biol Med / AJCN

P-1

Anthocyanins Are More Effective Post-Perturbation Than Pre-Loading

μ-rung · Lyapunov Reset · Timing Effect

Novel prediction Untested

dm³ Derivation

Contact-geometric basis

The μ operator corresponds to the transverse Lyapunov exponent μ_max = −2, which governs the rate of return to the limit cycle Γ = {r = 1} after the system has been displaced. A Lyapunov restoring force is only active when the system is away from equilibrium: if r < 1 (below the attractor), the μ-rung compound accelerates return. If r = 1 (at equilibrium), the restoring force has nothing to act on. Anthocyanins — classified at the μ rung — should therefore exert their strongest biological effect when the system is in a displaced (oxidatively stressed) state, not at baseline.

Falsifiable Hypothesis

H₁: Anthocyanin supplementation taken within 2 hours after acute oxidative stress (e.g., exhaustive exercise or high-fat meal challenge) produces greater reduction in inflammatory biomarkers (IL-6, CRP, F₂-isoprostanes) than the same dose taken 2 hours before the stressor.

H₀ (null): Timing of anthocyanin administration relative to oxidative challenge makes no significant difference to inflammatory biomarker reduction at 24 hours.

Minimum Viable Study Design

Design

3-arm crossover RCT
(pre-dose / post-dose / placebo)

n = 24 healthy adults
(power: 80%, α = 0.05, SD from Connolly 2006[R1])

Stressor

Eccentric exercise protocol (100 drop-jumps) or 1,000 kcal high-fat meal challenge

Intervention

480 mg anthocyanins (tart cherry concentrate, standardised) in 250 mL water

Primary endpoints

Serum IL-6, hsCRP, urinary F₂-isoprostanes at 0, 2, 6, 24 h

Washout

14 days between arms; low-polyphenol diet during washout

Cost estimate

~$12,000–18,000 USD (assay + supplement + subject compensation)

Timeline

6–8 months total; IRB + recruitment + 3 arms × 14-day washout

Expected Outcomes

Result	Interpretation	Publication path
Confirmed: post > pre effect	dm³ μ-rung timing prediction validated. Establishes that post-exercise anthocyanin dosing is the clinically correct protocol — contradicts current practice (most studies pre-load).	AJCN or J Nutr; high novelty — directly overturns prevailing pre-loading assumption.
Refuted: no timing difference	μ-rung timing model requires revision. Still publishable as negative RCT with well-powered design. Informs dm³ contact framework parameters.	Nutrients (negative RCT); valuable null result for the field.

Existing Literature Gap

Current anthocyanin RCTs almost uniformly administer supplements 1–2 hours before the exercise stressor (Connolly 2006, Bell 2014, Howatson 2010).[R1–R3] No published RCT has directly compared pre- vs. post-stressor timing as the primary variable. This is the gap. The dm³ framework provides the theoretical basis for predicting which timing will win and why.

Target journals: Am J Clin Nutr J Nutrition Nutrients (MDPI) Eur J Sport Sci

P-2

Dietary Chlorophyll Increases Intestinal B12 Precursor Production in Vegans

K-class Porphyrin Identity · Tetrapyrrole Scaffold

Novel prediction Untested

dm³ Derivation

Contact-geometric basis

The K operator classifies chlorophyll a (Mg-porphyrin), heme (Fe-porphyrin), and vitamin B12's corrinoid ring (Co-corrinoid) into the same K-class stratum: all three share the tetrapyrrole macrocycle scaffold. This is not metaphor — the biosynthetic pathway to B12 in gut bacteria branches from uroporphyrinogen III, the same intermediate used in chlorophyll and heme synthesis. Under the K contact-equivalence, providing dietary chlorophyll (the most abundant tetrapyrrole in a plant-based diet) should increase the availability of the shared biosynthetic scaffold to gut bacteria, potentially increasing bacterial B12 precursor synthesis.

Falsifiable Hypothesis

H₁: Vegan adults supplemented with 4 g/day spirulina (phycocyanin + chlorophyll, standardised tetrapyrrole content) for 8 weeks will show a statistically significant increase in serum holotranscobalamin II (active B12) and urinary methylmalonic acid reduction, compared to placebo, without any animal-derived B12 source.

H₀ (null): Chlorophyll-rich spirulina supplementation produces no significant change in B12 biomarkers in vegans.

⚠ Important caveat: Spirulina contains pseudovitamin B12 (adeninylcobamide) — analogues that can block true B12 absorption. The study must measure the corrinoid analogue fraction separately (HPLC-MS) to distinguish true B12 from analogues. This distinction is itself a novel methodological contribution.[R4]

Minimum Viable Study Design

Design

2-arm parallel RCT, double-blind, placebo-controlled

n = 30 per arm (60 total), confirmed vegans ≥ 1 year, B12 deficient at baseline (holotranscobalamin < 50 pmol/L)

Intervention

4 g/day spirulina (Arthrospira platensis, standardised ≥ 0.8 mg chlorophyll/g) vs. matched placebo (green-dyed cellulose powder)

Duration

8 weeks

Primary endpoints

Serum holotranscobalamin II (active B12), urinary methylmalonic acid (MMA), serum total B12

Secondary endpoints

Stool corrinoid HPLC-MS panel (distinguish true B12 vs. analogues), homocysteine, gut microbiome 16S rRNA sequencing

Key confounder controls

No fortified foods, no B12 supplements, dietary recall × 3, standardised meal plans

Cost estimate

~$35,000–55,000 USD (metabolomics, microbiome sequencing, clinical assays)

Expected Outcomes

Result	Interpretation	Publication path
Confirmed: holotrans-B12 ↑ in spirulina arm	First evidence that tetrapyrrole scaffold supplementation (chlorophyll) supports B12 biosynthesis in human gut microbiome. Major finding for plant-based nutrition — suggests whole-food chlorophyll sources (leafy greens, spirulina) have a role in B12 status beyond direct provision.	AJCN (high impact); potential press coverage given vegan relevance.
Refuted: no B12 change, or analogues confound	Clarifies the pseudovitamin B12 problem in spirulina — itself a publishable finding that directly warns against spirulina as a B12 source. Analogue profiling is novel regardless of primary outcome.	EJCN or Am J Clin Nutr negative RCT + analogue characterisation.

Target journals: Am J Clin Nutr Eur J Clin Nutr Plant Foods Hum Nutr J Nutr

P-3

High-Dose Lutein Competitively Suppresses Plasma Lycopene in a Dose-Dependent Manner

K-eigenstate Exclusion · Same-Stratum Competition at the F-Fold

Partially known Unquantified

dm³ Derivation

Contact-geometric basis

Lutein and lycopene occupy adjacent spectral bands (445/474 nm and 446/472/503 nm respectively) but share the same intestinal absorption transporter: NPC1L1 (Niemann-Pick C1-like 1 protein), the same transporter that mediates cholesterol absorption. Under the K-eigenstate model, when two nutrients share a transporter (= same K-classification machinery), they compete for uptake at the F-fold point (the intestinal epithelial barrier). High-dose supplementation of one should measurably suppress plasma levels of the other.

Falsifiable Hypothesis

H₁: Supplementation with 20 mg/day lutein for 8 weeks produces a statistically significant reduction in fasting plasma lycopene concentration (≥ 15% from baseline) compared to placebo, in adults with stable habitual tomato/lycopene intake.

Dose-response arm: 10 mg, 20 mg, 40 mg lutein; lycopene suppression proportional to lutein dose.

Minimum Viable Study Design

Design

4-arm parallel RCT (placebo, 10, 20, 40 mg lutein/day)

n = 20 per arm (80 total). Stable diet, no carotenoid supplements at baseline.

Duration

8 weeks supplementation + 4-week washout

Primary endpoint

Fasting serum lycopene (HPLC). Secondary: serum α- and β-carotene, zeaxanthin, β-cryptoxanthin (full carotenoid panel)

Diet control

3-day dietary recall at baseline and weeks 4, 8. Standardised tomato intake (1 cup canned tomatoes/day, provided).

Cost estimate

~$20,000–30,000 USD (HPLC carotenoid panels are relatively inexpensive)

Why This Matters Clinically

AREDS2 used 10 mg lutein + 2 mg zeaxanthin daily — doses within the range that, if P-3 is confirmed, could be suppressing lycopene levels in patients who also eat tomatoes regularly. A confirmed P-3 would mean that AREDS2 patients with high habitual lycopene intake were receiving a partially antagonistic supplementation regime without anyone knowing it. This has direct clinical implications for AMD supplementation protocols.[R5,R6]

Target journals: J Nutr Invest Ophthalmol Vis Sci Nutrients Lipids Health Dis

P-4

A Full-Spectrum Phytonutrient Blend Shows Superadditive Antioxidant Capacity

Contact Surjectivity · Theorem N.2 · Cross-Stratum ROS Specificity

Novel prediction Untested in vivo

dm³ Derivation

Contact-geometric basis

Theorem N.2 (Dietary Diversity = Contact Surjectivity) predicts that a diet covering all spectral strata activates every Legendrian stratum of the antioxidant ladder simultaneously. Different strata quench different reactive oxygen species: carotenoids (orange/red strata) quench singlet O₂ by energy transfer; vitamin C (orange stratum) reduces phenoxyl radicals; anthocyanins (violet stratum) scavenge superoxide and hydroxyl radical; polyphenols (UV stratum) inhibit lipid peroxidation chain reactions. These are mechanistically distinct, non-overlapping quenching pathways. When combined, the system should achieve superadditivity — the whole exceeds the sum of the parts — because each stratum handles ROS that the others cannot.

Falsifiable Hypothesis

H₁: A defined 6-compound blend covering all spectral strata (quercetin [UV] + cyanidin [violet] + phycocyanin [blue] + chlorophyllin [green] + β-carotene [orange] + lycopene [red]) will produce ORAC and FRAP values greater than the arithmetic sum of individual compound ORAC/FRAP values (superadditivity, interaction index > 1.0).

In vivo arm: the same blend will produce greater reduction in urinary 8-OHdG (DNA oxidation marker) than the highest single-compound dose.

Minimum Viable Study Design

Phase 1 (in vitro)

ORAC, FRAP, DPPH assays on each compound separately and in full 6-compound blend. Calculate combination index (CI). Cost: ~$2,000 in a food chemistry lab.

Phase 2 (in vivo, if P1 confirmed)

n = 30 crossover RCT: single compound (best performer from P1) vs. full blend vs. placebo. Primary: urinary 8-OHdG, plasma F₂-isoprostanes at 0, 4, 8 h post-dose.

Duration

Single acute dose crossover (3 arms × 7-day washout)

Phase 1 cost

~$2,000–4,000. Can be done in a university food science lab.

Phase 2 cost

~$15,000–25,000

Target journals: Food Chemistry Free Radic Biol Med AJCN J Agric Food Chem

P-5

Nutrients Show a Non-Linear Response Threshold at ≈ 1/3 of Standard Reference Intake

Stability Radius ε₀ = 1/3 · Attractor Basin Threshold

Novel prediction Untested as general law

dm³ Derivation

Contact-geometric basis

The dm³ stability radius^{[Ch 10]} ε₀ = 1/3 defines the minimum coupling strength below which the contact operator chain does not enter the attraction basin of the limit cycle Γ. Below ε₀, the trajectory disperses; above ε₀, it converges to the attractor. Applied to nutrient dose-response: the prediction is that for any nutrient with a well-defined biomarker response, the minimum effective dose (MED) — the threshold below which no statistically significant biomarker change occurs — will cluster near 1/3 of the established Dietary Reference Intake (DRI) or standard supplementation dose. This is testable as a cross-nutrient empirical regularity.

Falsifiable Hypothesis

H₁: Across a panel of ≥ 6 nutrients with established dose-response RCT data (magnesium, vitamin C, vitamin D3, curcumin, quercetin, EGCG), the minimum effective dose (defined as the lowest dose producing a statistically significant biomarker response vs. placebo) will fall within the range [0.25 × DRI, 0.42 × DRI] — i.e., within ±25% of the 1/3-DRI threshold — in ≥ 5/6 nutrients.

Equivalently: a meta-analysis of dose-response RCTs will show a statistically significant inflection point in the response curve at approximately 1/3 of the standard supplementation dose.

Minimum Viable Study Design

Design

Systematic review + meta-analysis of existing dose-response RCTs (no new trial required for first publication)

Nutrients to include

Mg (BP), Vit C (CRP), Vit D3 (25-OHD), curcumin (IL-6), quercetin (CRP), EGCG (LDL), EPA/DHA (TG), zinc (IL-1β)

Analysis

Non-linear dose-response meta-analysis (restricted cubic spline or fractional polynomial). Software: R (dosresmeta package).

Cost

~$0–3,000 (systematic review labour; no trial costs). Publishable as a standalone meta-analysis.

Timeline

3–5 months (literature search + extraction + analysis)

Why This Is Important

If confirmed, the ε₀ = 1/3 threshold would provide the first mathematical framework for predicting minimum effective doses across nutrient classes without running new trials — a significant tool for clinical nutrition and supplementation protocol design. It would also validate the contact-geometric framework as empirically productive in nutritional science, opening a research programme.[R7]

Target journals: Am J Clin Nutr Nutrients (meta-analysis) Br J Nutr Adv Nutr

P-6

The Fat-Soluble Absorption Window Is Maximised 30–45 min Post-Fat — Not Simultaneous

Whitney Fold Timing · Bile Salt Micelle Formation Kinetics

Partially known Optimal window unquantified

dm³ Derivation

Contact-geometric basis

The F operator (Whitney A₁ fold) fires when the system crosses the critical surface Γ — the point of irreversible metabolic commitment. For fat-soluble nutrients (carotenoids, vitamins D/K/E/A), the F-event is incorporation into bile salt mixed micelles in the intestinal lumen. Bile salt secretion peaks 15–30 minutes after fat ingestion. The mixed micelle formation (the fold surface Γ) is therefore reached approximately 20–40 minutes after a fat-containing meal. Presenting fat-soluble nutrients simultaneously with fat means they arrive at the fold point before the critical micelle concentration is reached. Introducing them 30–45 minutes later — after bile salts have peaked — should maximise the probability of micellar incorporation.

Falsifiable Hypothesis

H₁: β-Carotene (15 mg oral dose) consumed 35 minutes after a standardised fat-containing meal (20 g olive oil) will produce statistically significantly higher peak plasma β-carotene (C_max) and area under the curve (AUC₀₋₂₄h) compared to simultaneous ingestion with the same fat load and compared to ingestion 2 hours after fat.

Minimum Viable Study Design

Design

3-arm crossover (simultaneous / +35 min / +120 min)

n = 18 (adequate for pharmacokinetic crossover with AUC primary endpoint)

Standardised fat load

20 g olive oil in 150 mL warm water (emulsified) or standardised meal; identical across arms

Nutrient

15 mg β-carotene (pure, oil-dispersed capsule). Secondary: 10 mg lutein arm in crossover extension.

Blood sampling

Serum HPLC carotenoid panel at 0, 2, 4, 6, 8, 12, 24 h post-nutrient dose. Calculate C_max, T_max, AUC₀₋₂₄.

Cost

~$15,000–22,000 (pharmacokinetic study with serial blood draws)

Target journals: J Nutr Lipids Health Dis Nutrients Eur J Nutr

P-7

Evening Melatonin-Containing Foods Potentiate Next-Morning Fat-Soluble Nutrient Absorption

π-Rung Reeb Field Periodicity T* = 2π · Circadian Gut Motility

Novel prediction Untested

dm³ Derivation

Contact-geometric basis

Melatonin sits at the π rung of the contact ladder (period T* = 2π, the fundamental oscillatory frequency). The Reeb vector field R on the contact manifold flows with period T* — it is the circadian clock made geometric. Melatonin (MT1/MT2 receptors in the suprachiasmatic nucleus and gut) controls intestinal motility, bile acid secretion rhythms, and NPC1L1 transporter expression, all of which peak in the morning following evening melatonin secretion. Prediction: consuming melatonin-rich foods (tart cherry, walnuts) in the evening will upregulate morning intestinal bile acid secretion and NPC1L1 expression, producing measurably higher absorption of fat-soluble nutrients consumed the following morning.

Falsifiable Hypothesis

H₁: Adults consuming 240 mL tart cherry juice (standardised melatonin content ≥ 85 ng/serving) nightly for 2 weeks will show statistically significantly higher fasting plasma β-carotene and 25-OH-D₃ AUC following a standardised morning nutrient dose, compared to placebo cherry juice (colour/taste matched, melatonin-depleted).

Minimum Viable Study Design

Design

2-arm crossover RCT (tart cherry / placebo), 2-week run-in each arm

n = 24 (powered for pharmacokinetic AUC difference of 20%, SD from Howatson 2012)

Evening intervention

240 mL Montmorency tart cherry juice (Prunus cerasus, ~85 ng melatonin, standardised by HPLC) 1 hour before sleep

Morning probe

After 14 days: standardised breakfast (20 g olive oil) + 15 mg β-carotene + 2,000 IU D3. Serial blood draws 0–12 h.

Secondary endpoints

Urinary melatonin metabolite (6-sulfatoxymelatonin), Pittsburgh Sleep Quality Index (PSQI), morning serum bile acids

Cost

~$25,000–40,000 (2-arm × 14-day run-in + pharmacokinetic day)

Target journals: Chronobiol Int J Pineal Res Nutrients Eur J Nutr

P-8

Quercetin (φ-rung) + Anthocyanin (μ-rung) + Chlorophyllin (Δ-rung) Show Synergistic Anti-Inflammatory Effects

Adjacent Ladder Strata · n-Bonacci Sequential Activation

Novel prediction Untested combination

dm³ Derivation

Contact-geometric basis

The n-bonacci ladder proceeds φ → μ → η → Δ → Σ → Ω → τ. Adjacent rungs differ by the next n-bonacci step — a sequential escalation of the attractor depth. The contact-geometric prediction is that compounds at adjacent rungs activate sequentially in the operator chain: the φ-rung compound (quercetin, flavonol) initiates the NF-κB inhibition; the μ-rung (anthocyanins) resets the baseline via Nrf2 upregulation; the Δ-rung (chlorophyllin/sulforaphane) sustains Nrf2 activation through phase-II enzyme induction. These three operate on the same anti-inflammatory pathway but at consecutive contact strata — sequential, non-competing, cumulative. Combined, they should show synergy (combination index CI < 1) rather than additivity or antagonism.

Falsifiable Hypothesis

H₁: The combination of quercetin (500 mg) + cyanidin-3-glucoside (250 mg) + chlorophyllin (100 mg) will reduce serum IL-6 and NF-κB p65 nuclear translocation (in ex vivo stimulated PBMCs) significantly more than any of the three compounds alone at equivalent doses, with a combination index CI < 1.0 (synergy) by the Chou-Talalay method.

Minimum Viable Study Design

Phase 1 (ex vivo)

PBMCs from 6 healthy donors. LPS-stimulated (100 ng/mL). Dose-response matrix: each compound × 5 doses × 3-compound combination. CI by Chou-Talalay. Cost ~$3,000.

Phase 2 (in vivo, if P1 synergistic)

4-arm factorial RCT: placebo / quercetin alone / Q+An / Q+An+Chl. n = 36 (9/arm). Acute LPS endotoxin challenge model (0.1 ng/kg IV) or exercise model. Primary: serum IL-6 at 2 h.

Phase 1 cost

~$3,000–6,000 (PBMC isolation + cytokine ELISA + compound standardisation)

Phase 2 cost

~$30,000–50,000

Expected Outcomes

Result	Interpretation
CI < 1.0 (synergy)	First experimental validation of dm³ ladder-adjacency as a predictor of compound synergy. Establishes a general principle: combine compounds at sequential ladder rungs for synergistic anti-inflammatory effects. High-impact publication.
CI = 1.0 (additive)	Adjacent rungs are independent, not synergistic. Refines ladder model — contact-geometric independence rather than sequential coupling. Still publishable: defines limits of combination therapy prediction.
CI > 1.0 (antagonism)	Unexpected — would suggest that quercetin and anthocyanins compete even across strata. Major finding requiring mechanistic follow-up.

Target journals: Free Radic Biol Med Redox Biol AJCN J Funct Foods

To-Do: Setting Up Claude as a Research Partner

Claude is a useful research partner for working with this material — but only if it is set up correctly. By default, Claude will evaluate novel theoretical frameworks against published consensus and flag anything that lacks peer-reviewed backing. That is the wrong mode for exploratory research. The goal here is not to defend dm³ as established science; it is to use it as a hypothesis generator and let the experiments decide.

The prompts below set Claude into a Socratic research-partner mode: it helps you understand the predictions, find relevant literature, design experiments, and stress-test hypotheses — without spending energy attacking the framework for being novel. Copy these prompts verbatim into a new Claude session. The session priming prompt (Prompt 0) must come first.

📚

Which Books of the Series to Read First

Reading path for a nutrition / dietetics student

Start Here — Book III (Applied Biology, The Mini-Beast)

This is the entry point for nutrition students. No advanced mathematics required. These chapters are self-contained and connect the dm³ operator chain directly to biological systems you already know.

Chapter	What it covers	Why read it first
ch-nutrient-spectrum.html	Periodic table of phytonutrients, spectroscopic data, 43 peer-reviewed references	The empirical foundation. Read this before the predictions.
ch-nutrient-predictions.html	This document — 8 falsifiable predictions with study designs	Your research agenda.
ch5-immune.html	Immune memory as a contact-geometric attractor	Connects to P-8 (adjacent-stratum synergy and immune biomarkers).
ch6-resonance.html	Cardiac resonance and the Reeb field	Context for how the operator chain governs organ-level dynamics.
ch3-circadian.html	Circadian rhythms as contact periodicity	Essential for P-7 (melatonin / π-rung timing).

Then — Book I (Vol I, GOMC) · The Mathematical Framework

Read this to understand where the operator chain G = U ∘ F ∘ K ∘ C comes from and why ε₀ = 1/3 and τ = 2 are not arbitrary. You do not need to follow the proofs — read the prose sections and the theorem statements.

Chapter	What it covers
ch01-one-equation.html	The single equation that underlies all operator-chain predictions
ch01-dm3-framework.html	Overview of G = U∘F∘K∘C; ε₀, μ_max, τ defined
ch9-phi.html	The Fibonacci operator φ; why 1.618 appears in biological scaling
chPI-recurrence.html	The π rung; T* = 2π as the fundamental biological period

Optional Depth — Greek Operator Chapters

Each Greek-coded chapter develops one rung of the n-bonacci ladder. Read these to understand why the ladder predicts sequential activation and why adjacent strata should synergise (P-8).

Chapter	Rung	Nutritional connection
chMu-lyapunov.html	μ = −2 (Lyapunov reset)	Why anthocyanins are post-perturbation compounds (P-1)
chEta-tribonacci.html	η ≈ 1.839	Three-strand coupling; spirulina B2 / phycocyanin (P-2)
chDelta-tetranacci.html	Δ ≈ 1.927	Chlorophyll / Mg / sulforaphane (P-2, P-8)
chOmega-hexabonacci.html	Ω → τ = 2	β-carotene / lycopene at the embodiment threshold

💬

Claude Prompt Library — Research Partner Mode

Copy each prompt into a new Claude session · use in order

Prompt 0 — Session Priming (use this first, always)

This prompt sets the epistemological frame. Without it, Claude will spend energy questioning whether dm³ is peer-reviewed rather than helping you use it.

        I am a nutrition student working with a theoretical framework called dm³
        (contact-geometric theory of generative transitions, Principia Orthogona series,
        G6 LLC, doi:10.5281/zenodo.19117399). The framework generates mechanistic
        predictions about nutrient behaviour — timing effects, competitive absorption,
        synergistic combinations, dose thresholds.

        I am using it as a hypothesis generator, not as established science.
        My goal is to test these predictions experimentally and publish findings.

        For this session, please act as a Socratic research partner.
        Your job is to:

        1. Help me understand each prediction mechanistically

        2. Find existing peer-reviewed literature that is relevant (supporting or challenging)

        3. Help me refine study designs

        4. Stress-test the hypotheses constructively ("what would make this stronger?") rather than dismissively ("this is not established")

        Do not evaluate whether the dm³ framework itself is valid — the experiments will do that.
        Treat it as you would any theoretical model that generates falsifiable predictions:
        useful if it predicts something true, revisable if it does not.

        Ready?

Prompt 1 — Understanding a Prediction

Use after Prompt 0. Replace [P-X] with the prediction number.

        Walk me through prediction [P-X] in plain language.
        I understand basic nutrition science but not contact geometry.
        Explain:

        - What the dm³ framework is saying about this nutrient / combination / timing

        - What the biological mechanism would be in standard nutrition terms

        - What already published research comes closest to testing this

        - What the key variables are that I would need to measure

        Then ask me one question to check I have understood it correctly before we move on.

Prompt 2 — Literature Search Setup

Use this to build a PubMed search strategy for any prediction.

        I want to do a systematic literature search for prediction [P-X].
        The hypothesis is: [paste the H₁ statement from the prediction card].

        Please give me:

        1. A PubMed search string (PICO format: Population, Intervention, Comparator, Outcome)

        2. Three to five additional search terms I might miss if I search the obvious terms only

        3. Two or three key papers I should read first to understand the existing evidence base

        4. The most important gap in the literature — what nobody has measured yet

        Do not tell me whether the hypothesis is likely to be true.
        Just help me map the evidence landscape.

Prompt 3 — Study Design Review

Use when you want to refine the study design for a specific prediction.

        I want to design a study to test prediction [P-X].
        The proposed design is: [paste the study design from the prediction card].

        Review this design and tell me:

        1. Is the sample size justification solid? What power calculation would a reviewer expect?

        2. What are the three most likely sources of confounding I have not controlled for?

        3. What are the key inclusion and exclusion criteria I need to specify in an IRB application?

        4. What is the weakest point of the design — the thing most likely to draw a rejection from a peer reviewer?

        5. What is one lower-cost version of this study I could run first as a pilot?

        Be specific. I will use your feedback to write a research proposal.

Prompt 4 — Constructive Challenge

Use this to stress-test a prediction before investing in a study. Invite the criticism before a reviewer does.

        I want you to challenge prediction [P-X] as hard as you can —
        but constructively. Imagine you are a rigorous peer reviewer who is
        open to novel ideas but demands mechanistic clarity and methodological
        rigour.


        Tell me:

        1. What is the strongest alternative explanation for the predicted effect
           that does not require the dm³ framework?

        2. What confounders could produce the predicted result even if the hypothesis is wrong?

        3. What prior studies, if any, have tested something similar and what did they find?

        4. What additional measurement or control group would make the study
           definitively convincing rather than merely suggestive?


        After challenging it, tell me: if you were a reviewer, what would it take
        for you to accept this paper?
      

Prompt 5 — Writing the Introduction Section

Use when you are ready to write. This keeps Claude in helper mode, not author mode.

        I am writing the introduction section of a paper testing prediction [P-X].
        The target journal is [journal name]. The word limit for the introduction is approximately [N] words.

        Help me structure the introduction. I will write it — do not write it for me.
        Give me:

        1. A three-paragraph outline: what goes in each paragraph, and why, in this journal's style

        2. The three key citations I must include (give me the full reference, not just the author)

        3. One sentence that states the gap in the literature — the reason my study is needed

        4. One sentence that states the study aim, using precise outcome language

        After I write a draft, I will share it with you for feedback.

Prompt 6 — After a Negative Result

If your experiment refutes the prediction — do not discard the result. This prompt helps you publish a null finding.

        My experiment did not confirm prediction [P-X]. The hypothesis was [H₁].
        The observed result was [describe result].

        Help me understand what this means:

        1. Does this refute the prediction entirely, or does it refute only under the specific conditions I tested?

        2. What alternative explanation does this result point toward?

        3. Is this publishable as a null result? Which journals specifically publish well-designed null results in nutrition?

        4. What should the title of a null-result paper look like for this finding?

        5. How does this result revise the prediction — is there a more precise version of the hypothesis that my result is consistent with?

        A well-powered null result is as valuable as a confirmation. Help me write it that way.

Prompt 7 — Connecting to the Full Series (for deeper study)

Use when you want to understand the mathematical framework more deeply — not required for research, but valuable for understanding why the predictions take the form they do.

        I am studying the dm³ framework more deeply and have been reading
        [chapter name or topic] from the Principia Orthogona series.

        I am not a mathematician. I understand biology and chemistry at graduate level.
        Explain to me:

        1. What does "contact geometry" mean in plain language, using a biological analogy?

        2. What is the Reeb vector field and why does it correspond to a biological trajectory?

        3. Why is the stability radius ε₀ = 1/3 rather than some other value?

        4. What does it mean mathematically that the n-bonacci ladder converges to τ = 2?

        Use analogies from nutrition or physiology wherever possible.
        Stop and check my understanding before moving to the next point.

When Claude Pushes Back on the Framework

Even with Prompt 0 in place, Claude may occasionally revert to skepticism about dm³ — noting that it is not peer-reviewed or that contact geometry is not standard nutrition theory. When this happens, use this redirect:

        I understand that dm³ is not yet peer-reviewed as a complete framework.
        That is the point — I am the researcher who will test it.
        Please stay in research-partner mode: help me design the test,
        not evaluate whether the theory is correct.
        The experiment is how we find out.
      

A Note on Novelty and Attribution

Each prediction above is derived from the dm³ contact-geometric framework (Principia Orthogona, G6 LLC, 2026; doi:10.5281/zenodo.19117399). Researchers using these predictions in published work are encouraged to cite the framework as the theoretical source. The appropriate citation is:

Suggested framework citation

Grossi PN. Principia Orthogona: Contact-Geometric Theory of Generative Transitions. G6 LLC, Newark NJ. 2026. Series concept DOI: 10.5281/zenodo.19117399. ORCID 0009-0000-6496-2186. Contact: g6llc@proton.me.

This citation does not affect the nutritionist's own authorship of the experimental findings — it simply acknowledges the mathematical framework that generated the hypothesis, in the same way a drug trial cites the pharmacodynamic theory that predicted the drug's mechanism.

Findings that confirm or refute these predictions should also be shared with the author at g6llc@proton.me to update the prediction registry and advance the framework's empirical validation programme.

References for Study Design

[R1] Connolly DA, et al. Efficacy of a tart cherry juice blend in preventing the symptoms of muscle damage. Br J Sports Med. 2006; 40(8):679–683.
[R2] Bell PG, et al. The role of cherries in exercise and health. Scand J Med Sci Sports. 2014; 24(3):477–490.
[R3] Howatson G, et al. Influence of tart cherry juice on indices of recovery following marathon running. Scand J Med Sci Sports. 2010; 20(6):843–852.
[R4] Watanabe F, et al. Pseudovitamin B12 is the predominant cobamide of an algal health food, spirulina tablets. J Agric Food Chem. 1999; 47(11):4736–4741.
[R5] AREDS2 Research Group. Lutein + zeaxanthin and omega-3 fatty acids for age-related macular degeneration: AREDS2 RCT. JAMA. 2013; 309(19):2005–2015.
[R6] Khachik F, et al. Lutein, lycopene, and their oxidative metabolites in chemoprevention of cancer. J Cell Biochem Suppl. 1995; 22:236–246.
[R7] Aggett PJ, et al. Consensus document. Dose-response relationships in nutritional science. Br J Nutr. 1999; 81(5):347–351.
[R8] Chou TC. Drug combination studies and their synergy quantification using the Chou-Talalay method. Cancer Res. 2010; 70(2):440–446.
[R9] Howatson G, et al. Effect of tart cherry juice on melatonin levels and improved sleep quality. Eur J Nutr. 2012; 51(8):909–916.
[R10] Parker RS. Absorption, metabolism, and transport of carotenoids. FASEB J. 1996; 10(5):542–551.

Nutrient Prediction Registry Eight Mechanistically-Derived Hypotheses

Prediction Overview

dm³ Derivation

Falsifiable Hypothesis

Minimum Viable Study Design

Expected Outcomes

Existing Literature Gap

dm³ Derivation

Falsifiable Hypothesis

Minimum Viable Study Design

Expected Outcomes

dm³ Derivation

Falsifiable Hypothesis

Minimum Viable Study Design

Why This Matters Clinically

dm³ Derivation

Falsifiable Hypothesis

Minimum Viable Study Design

dm³ Derivation

Falsifiable Hypothesis

Minimum Viable Study Design

Why This Is Important

dm³ Derivation

Falsifiable Hypothesis

Minimum Viable Study Design

dm³ Derivation

Falsifiable Hypothesis

Minimum Viable Study Design

dm³ Derivation

Falsifiable Hypothesis

Minimum Viable Study Design

Expected Outcomes

To-Do: Setting Up Claude as a Research Partner

Start Here — Book III (Applied Biology, The Mini-Beast)

Then — Book I (Vol I, GOMC) · The Mathematical Framework

Optional Depth — Greek Operator Chapters

Prompt 0 — Session Priming (use this first, always)

Prompt 1 — Understanding a Prediction

Prompt 2 — Literature Search Setup

Prompt 3 — Study Design Review

Prompt 4 — Constructive Challenge

Prompt 5 — Writing the Introduction Section

Prompt 6 — After a Negative Result

Prompt 7 — Connecting to the Full Series (for deeper study)

When Claude Pushes Back on the Framework

A Note on Novelty and Attribution

References for Study Design

Nutrient Prediction Registry
Eight Mechanistically-Derived Hypotheses