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Record W3080680255 · doi:10.1101/2020.08.25.266163

A physical theory of movement in small animals

2020· preprint· en· W3080680255 on OpenAlexafffund
Jane Loveless, Alastair Garner, Abdul-Raouf Issa, Ruairí J.V. Roberts, Barbara Webb, Lucia L. Prieto-Godino, Tomoko Ohyama, Claudio R. Alonso

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsMcGill University
FundersMedical Research CouncilCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaWellcome Trust
KeywordsCrawlingStatistical physicsClassical mechanicsPhysicsStability (learning theory)Space (punctuation)Computer scienceMathematicsBiologyAnatomy

Abstract

fetched live from OpenAlex

Abstract All animal behaviour must ultimately be governed by physical laws. As a basis for understanding the physics of behaviour in a simple system, we here develop an effective theory for the motion of the larval form of the fruitfly Drosophila melanogaster, and compare it against a quantitative analysis of the real animal’s behaviour. We first define a set of fields which quantify stretching, bending, and twisting along the larva’s antero- posterior axis, and then perform a search in the space of possible theories that could govern the long-wavelength physics of these fields, using a simplified approach inspired by the renormalisation group. Guided by symmetry considerations and stability requirements, we arrive at a unique, analytically tractable free-field theory with a minimum of free parameters. Unexpectedly, we are able to explain a wide-spectrum of features of Drosophila larval behaviour by applying equilibrium statistical mechanics: our theory closely predicts the animals’ postural modes (eigenmaggots), as well as distributions and trajectories in the postural mode space across several behaviours, including peristaltic crawling, rolling, self-righting and unbiased substrate exploration. We explain the low-dimensionality of postural dynamics via Boltzmann suppression of high frequency modes, and also propose and experimentally test, novel predictions on the relationships between different forms of body deformation and animal behaviour. We show that crawling and rolling are dominated by similar symmetry properties, leading to identical dynamics/statistics in mode space, while rolling and unbiased exploration have a common dominant timescale. Furthermore, we are able to demonstrate that self-righting behaviour occurs continuously throughout substrate exploration, owing to the decoupling of stretching, bending, and twisting at low energies. Together, our results demonstrate that relatively simple effective physics can be used to explain and predict a wide range of animal behaviours.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.003
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.230
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

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