MétaCan
Menu
Back to cohort
Record W1590251454 · doi:10.1002/9780470319390.ch1

The Evolutionary Developmental Biology of Tinkering: An Introduction to the Challenge

2006· review· en· W1590251454 on OpenAlexaff
Daniel E. Lieberman, Brian K. Hall

Bibliographic record

VenueNovartis Foundation symposium · 2006
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBiologyEvolutionary biologyEvolutionary developmental biologyPhenotypeEpigeneticsNatural selectionDevelopmental biologyBody planSelection (genetic algorithm)Phenotypic traitEpigenesisDevelopmental geneticsPhenotypic plasticityHuman evolutionary geneticsGeneticsPhylogeneticsGeneRegulation of gene expressionDNA methylation

Abstract

fetched live from OpenAlex

Recent developments in evolutionary biology have conflicting implications for our understanding of the developmental bases of microevolutionary processes. On the one hand, Darwinian theory predicts that evolution occurs mostly gradually and incrementally through selection on small-scale, heritable changes in phenotype within populations. On the other hand, many discoveries in evolutionary developmental biology--quite a few based on comparisons of distantly related model organisms--suggest that relatively simple transformations of developmental pathways can lead to dramatic, rapid change in phenotype. Here I review the history of and bases for gradualist versus punctuationalist views from a developmental perspective, and propose a framework with which to reconcile them. Notably, while tinkering with developmental pathways can underlie large-scale transformations in body plan, the phenotypic effect of these changes is often modulated by the complexity of the genetic and epigenetic contexts in which they develop. Thus the phenotypic effects of mutations of potentially large effect can manifest themselves rapidly, but they are more likely to emerge more incrementally over evolutionary time via transitional forms as natural selection within populations acts on their expression. To test these hypotheses, and to better understand how developmental shifts underlie microevolutionary change, future research needs to be directed at understanding how complex developmental networks, both genetic and epigenetic, structure the phenotypic effects of particular mutations within populations of organisms.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.316
Teacher spread0.292 · 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 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

Citations10
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueNovartis Foundation symposiumSame topicEvolution and Genetic DynamicsFrench-language works237,207