Statolith Size Polydispersity as Natural Experiment — Larger Statoliths Carry More Fisher Information Per Unit Mass
Bigger plant gravity sensors may pack exponentially more information — and math predicts exactly how much.
Cramer-Rao bound / Fisher information from statistical estimation theory applied to statolith-based gravity sensing in plants
6 bridge concepts›
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6-Dimension Weighted Scoring
Each hypothesis is scored across 6 dimensions by the Ranker agent, then verified by a 10-point Quality Gate rubric. A +0.5 bonus applies for hypotheses crossing 2+ disciplinary boundaries.
Is the connection unexplored in existing literature?
How concrete and detailed is the proposed mechanism?
How far apart are the connected disciplines?
Can this be verified with existing methods and data?
If true, how much would this change our understanding?
Are claims supported by retrievable published evidence?
Composite = weighted average of all 6 dimensions. Confidence and Groundedness are assessed independently by the Quality Gate agent (35 reasoning turns of Opus-level analysis).
RQuality Gate Rubric
0/6 PASS · 6 CONDITIONAL
| Criterion | Result |
|---|---|
| Impact | 5 |
| Novelty | 7 |
| Testability | 6 |
| Groundedness | 5 |
| Cross Domain Creativity | 7 |
| Mechanistic Specificity | 7 |
Claim Verification
Empirical Evidence
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The Empirical Evidence Score measures independent real-world signals that converge with a hypothesis — not cited by the pipeline, but discovered through separate search.
Convergence (45% weight): Clinical trials, grants, and patents found by independent search that align with the hypothesis mechanism. Strong = direct mechanism match.
Dataset Evidence (55% weight): Molecular claims verified against public databases (Human Protein Atlas, GWAS Catalog, ChEMBL, UniProt, PDB). Confirmed = data matches the claim.
Plants can sense gravity — that's how roots grow down and shoots grow up. They do this using tiny, dense starch granules called statoliths that settle under gravity inside specialized cells, a bit like a built-in snow globe that always shows which way is 'down.' What's puzzling is that these granules come in a range of sizes, even within the same plant. Why the variation? Is it noise in the system, or does it serve a purpose? This hypothesis borrows a powerful idea from statistics called the Cramér-Rao bound — essentially a mathematical law that sets a fundamental limit on how precisely any sensor can estimate a signal, no matter how clever you are. The hypothesis proposes that when you apply this framework to statoliths, you get a striking prediction: the information a statolith carries about gravitational direction scales with the *sixth power* of its radius. That's an enormous advantage for larger granules — double the radius and you get 64 times more useful signal. This would mean that size variation in statoliths isn't just biological messiness; it could be a tunable feature, with the population of granule sizes acting like an array of sensors with very different sensitivities. The real cleverness here is treating a cell full of differently-sized granules as a 'heterogeneous sensor array' — a concept from engineering applied to biology. If the math holds, it would give researchers a precise, testable framework for why statoliths are the size they are, and whether plants with bigger statoliths are genuinely better at detecting subtle tilts.
This is an AI-generated summary. Read the full mechanism below for technical detail.
Why This Matters
If confirmed, this could give plant biologists a quantitative design principle for gravity sensing — explaining why statolith size varies across species and whether breeding or engineering plants with larger statoliths could make them more responsive to gravity, which matters for crop resilience when plants are grown in unusual orientations or low-gravity environments like space. It could also inform the design of bio-inspired micro-sensors, where polydisperse particle arrays might outperform uniform ones. The specific r^6 scaling prediction is sharp enough to be tested with existing confocal microscopy and particle-tracking tools, making this a relatively low-cost hypothesis to probe. Even a partial confirmation would mark the first time information theory has been rigorously applied to understand the physical limits of how plants navigate their world.
Mechanism
Hypothesis: Statolith Size Polydispersity as Natural Experiment — Larger Statoliths Carry More Fisher Information Per Unit Mass. Mechanistic specificity: Specific r^6 scaling prediction. Specific measurement protocol (individual tracking). Specific cross-species prediction. Cross-domain creativity: Heterogeneous sensor array theory applied to polydisperse organelle population. Impact: Provides quantitative framework for statolith size optimization but narrower impact than the cross-species or allelic series hypotheses.
Supporting Evidence
Key strength: r^6 scaling is a mathematically rigorous, distinctive prediction. Groundedness: Size variation visible in published confocal images. Physics (lambda ~ 1/r^3) is grounded. But correlation correction for large particles is not addressed. Novelty: NOVEL — no paper analyzes Fisher information as function of statolith radius
How to Test
Testability assessment: Individual statolith tracking with modern confocal is feasible. r^6 variance scaling is a distinctive prediction. Cross-species amyloplast size comparison requires data compilation. Key risk: Hydrodynamic correlations between large particles may reduce per-particle information advantage
Other hypotheses in this cluster
Starchless Mutant Allelic Series as Quantitative Test of CRB N-Scaling
PASSCounting starch granules in plant cells could reveal the mathematical limits of how plants sense gravity.
Cross-Species CRB Landscape Predicts Gravitropic Precision Hierarchy Across Statolith-Based Plant Organs
PASSA math formula from statistics could predict exactly how precisely different plants sense gravity — and why some are better at it than others.
CRB Framework Makes Testable Predictions at 1-10 Degree Range Through N-Dependent Precision Scaling
PASSA statistics theorem from the 1940s may reveal the fundamental precision limits of how plants sense gravity.
Information-Geometric Phase Transition Predicts Mutant-Specific Threshold Shifts in Gravitropic Dose-Response
CONDITIONALA math theory used in spy satellites could reveal why plants know which way is down — with a precise prediction to test it.
Information Bottleneck Matching in Gravitropic Cascade Revealed by Single-Factor Perturbation Asymmetry
CONDITIONALPlants may have evolved perfectly matched signal-processing steps to sense gravity as efficiently as physics allows.
Can you test this?
This hypothesis needs real scientists to validate or invalidate it. Both outcomes advance science.