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Record W2551410878 · doi:10.1098/rstb.2015.0417

What determines direction of asymmetry: genes, environment or chance?

2016· review· en· W2551410878 on OpenAlexafffund
A. Richard Palmer

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2016
Typereview
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyGeneAsymmetryEvolutionary biologyGeneticsNeuroscienceComputational biologyPhysics

Abstract

fetched live from OpenAlex

Conspicuous asymmetries seen in many animals and plants offer diverse opportunities to test how the development of a similar morphological feature has evolved in wildly different types of organisms. One key question is: do common rules govern how direction of asymmetry is determined (symmetry is broken) during ontogeny to yield an asymmetrical individual? Examples from numerous organisms illustrate how diverse this process is. These examples also provide some surprising answers to related questions. Is direction of asymmetry in an individual determined by genes, environment or chance? Is direction of asymmetry determined locally (structure by structure) or globally (at the level of the whole body)? Does direction of asymmetry persist when an asymmetrical structure regenerates following autotomy? The answers vary greatly for asymmetries as diverse as gastropod coiling direction, flatfish eye side, crossbill finch bill crossing, asymmetrical claws in shrimp, lobsters and crabs, katydid sound-producing structures, earwig penises and various plant asymmetries. Several examples also reveal how stochastic asymmetry in mollusc and crustacean early cleavage, in Drosophila oogenesis, and in Caenorhabditis elegans epidermal blast cell movement, is a normal component of deterministic development. Collectively, these examples shed light on the role of genes as leaders or followers in evolution.This article is part of the themed issue 'Provocative questions in left-right asymmetry'.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.174
GPT teacher head0.355
Teacher spread0.181 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations53
Published2016
Admission routes2
Has abstractyes

Explore more

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