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Record W2474232183 · doi:10.1093/icb/icw053

Evolutionary Conflict Between Maternal and Paternal Interests: Integration with Evolutionary Endocrinology

2016· review· en· W2474232183 on OpenAlexaff
Mikael Mökkönen, Esa Koskela, Tapio Mappes, Suzanne C. Mills

Bibliographic record

VenueIntegrative and Comparative Biology · 2016
Typereview
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsSimon Fraser University
FundersUniversity of New England
KeywordsMaladaptationBiologySexual conflictOffspringEvolutionary ecologyEvolutionary biologyAdaptation (eye)Reproductive successBank voleTestosterone (patch)Human evolutionary geneticsTerritorialityEcologyGeneticsSexual selectionDemographyPhylogeneticsPopulationNeuroscienceGene

Abstract

fetched live from OpenAlex

Conflict between mates, as well as conflict between parents and offspring are due to divergent evolutionary interests of the interacting individuals. Hormone systems provide genetically based proximate mechanisms for mediating phenotypic adaptation and maladaptation characteristic of evolutionary conflict between individuals. Testosterone (T) is among the most commonly studied hormones in evolutionary biology, and as such, its role in shaping sexually dimorphic behaviors and physiology is relatively well understood, but its role in evolutionary conflict is not as clear. In this review, we outline the genomic conflicts arising within the family unit, and incorporate multiple lines of evidence from the bank vole (Myodes glareolus) system to outline how T impacts traits associated with reproduction and survival, resulting in a sexually antagonistic genetic trade-off in fitness. A major prediction arising from this work is that lower T is favored in females, whereas the optimal T level in males fluctuates in relation to social and ecological factors. We additionally discuss future directions to further integrate endocrinology into the study of sexual and parent-offspring conflicts.

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), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.177
GPT teacher head0.434
Teacher spread0.257 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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