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Evolutionary Developmental Biology

2021· reference-entry· en· W4210874410 on OpenAlexaff

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsEvolutionary developmental biologyBiologySubject (documents)Evolutionary biologyField (mathematics)PopulationSet (abstract data type)Cognitive scienceComputer scienceSociologyPsychology

Abstract

fetched live from OpenAlex

Evolutionary developmental biology, or evo-devo, is the study of the reciprocal relationships between ontogenetic development and evolutionary processes. This still relatively new research field, of roughly four decades, is highly heterogeneous and based on a variety of different approaches and interpretations of evo-devo as a research field. Broadly conceived forms of evo-devo, in which nearly every comparative-embryological or developmental-genetic approach is presumed to have evolutionary significance, intersect with more specialized practices that are characterized by the explicit evolutionary questions they attempt to answer. In this bibliographic survey we focus on the latter. These works explore an interconnected set of two principal scientific problems: How do the mechanisms of individual development evolve, and how do the properties of developmental systems that characterize organismal lineages influence their further evolution? Within each of these larger areas, a host of more detailed questions can be defined, and, in pursuing them, evo-devo addresses many empirical and conceptual issues that pertain to the emergence of complex phenotypes as well as the evolving interactions of development with population-level processes and the environment. The theoretical consequences of these kinds of investigations have a significant impact on how organismal evolution is conceptualized today. Thus, the publications listed herein were chosen for their specific evo-devo content and their capacity to bridge the empirical and theoretical dimensions. Recent works are favored, but foundational classics of individual subject areas are also cited. Besides the general parts, our survey contains sixteen thematic sections that cover the most important areas of evo-devo research. For each of these sections, we were permitted to list up to ten publications. Of course, this cannot do justice to all the excellent work in the field. Therefore, we attempted to highlight publications that are representative of the selected areas and address crucial conceptual aspects. Since even this criterion is a highly subjective one, we ask all those whose work could not be included for their understanding. In addition to providing an introduction to the characteristic themes of evo-devo, we have aimed for a suitability of this survey as a comprehensive resource for the teaching of evo-devo to advanced undergraduate and graduate students.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.061
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0610.022

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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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