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Record W3202165765 · doi:10.1111/evo.14369

Partial paternity does not always select for female‐biased care

2021· article· en· W3202165765 on OpenAlexaff
Priya Iyer, Abhishek Shukla

Bibliographic record

VenueEvolution · 2021
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversity of Alberta
FundersDepartment of Science and Technology, Ministry of Science and Technology, IndiaDepartment of Biotechnology, Ministry of Science and Technology, IndiaIndian Institute of Science
KeywordsGeneralityBiologyPaternal careMatingGuard (computer science)Selection (genetic algorithm)OffspringZoologyGeneticsPregnancyPsychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The theoretical literature predicts that parentage differences between the sexes, due to females mating with multiple males, select males to provide less parental care and females to care more for the offspring. We formulate simple evolutionary games to question the generality of this prediction. We find that the relationship between paternal care and fitness gained from extra-pair matings is important. A trade-off between these two quantities is required for partial paternity and complete maternity to bias the evolutionary stable strategy (ESS) toward more female care. We argue that this trade-off has been implicitly or explicitly assumed in most previous theories. However, if there is no trade-off between paternal care and extra-pair matings, parentage differences do not influence the ESS sex roles. Moreover, it is also possible for these two quantities to have a positive relationship, in which case we predict selection for male care is possible. We support these predictions using agent-based simulations. We also consider the possibility that caring males have greater opportunities to guard their paternity, and find that this mechanism can also select for male-biased care. Hence, we derive the conditions under which male care may be selected despite partial paternity and complete maternity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.998

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)0.0030.001

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 head0.046
GPT teacher head0.347
Teacher spread0.301 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2021
Admission routes1
Has abstractyes

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