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Beyond the phenotypic gambit: molecular behavioural ecology and the evolution of genetic architecture

2011· review· en· W1877706480 on OpenAlexafffund
Stevan A. Springer, Bernard J. Crespi, Willie J. Swanson

Bibliographic record

VenueMolecular Ecology · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsSimon Fraser University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyGenetic architectureEvolutionary biologyPhenotypeSelection (genetic algorithm)Phenotypic traitMechanism (biology)Human evolutionary geneticsAdaptation (eye)Natural selectionVariation (astronomy)Molecular evolutionGeneGeneticsNeuroscienceGenomeArtificial intelligence

Abstract

fetched live from OpenAlex

Most studies of behaviour examine traits whose proximate causes include sensory input and neural decision-making, but conflict and collaboration in biological systems began long before brains or sensory systems evolved. Many behaviours result from non-neural mechanisms such as direct physical contact between recognition proteins or modifications of development that coincide with altered behaviour. These simple molecular mechanisms form the basis of important biological functions and can enact organismal interactions that are as subtle, strategic and interesting as any. The genetic changes that underlie divergent molecular behaviours are often targets of selection, indicating that their functional variation has important fitness consequences. These behaviours evolve by discrete units of quantifiable phenotypic effect (amino acid and regulatory mutations, often by successive mutations of the same gene), so the role of selection in shaping evolutionary change can be evaluated on the scale at which heritable phenotypic variation originates. We describe experimental strategies for finding genes that underlie biochemical and developmental alterations of behaviour, survey the existing literature highlighting cases where the simplicity of molecular behaviours has allowed insight to the evolutionary process and discuss the utility of a genetic knowledge of the sources and spectrum of phenotypic variation for a deeper understanding of how genetic and phenotypic architectures evolve.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.244
Teacher spread0.236 · 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.

Study designNot applicable
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

Citations48
Published2011
Admission routes2
Has abstractyes

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