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Record W4405218985 · doi:10.1093/evolut/qpae175

Selective advantage of redirected helping in a viscous population

2024· article· en· W4405218985 on OpenAlexafffund
Alan Flatrès, Geoff Wild

Bibliographic record

VenueEvolution · 2024
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBroodBiologyPopulationSurvival of the fittestBiological dispersalFecundityInclusive fitnessHelping behaviorEcologyEvolutionary biologyDemographySociology

Abstract

fetched live from OpenAlex

When a brood fails, the failed parent can help a neighbor rear its offspring. This behavior is known as redirected helping and occurs in various species. The advantage of redirected helping may seem obvious, provided the individual whose brood fails helps a related neighbor: The helper at least gains indirect fitness by redirecting its parental effort. However, complications arise when considering a viscous population, where individuals remain on or close to their natal site. In such a population, individuals compete with relatives, which dilutes the advantage of helping and may counteract it altogether. This raises a question: when can we expect redirected helping to evolve in a viscous population? We address this question with inclusive fitness models. We find that redirected helping can always be favored in a viscous population, provided the cost is sufficiently low. We also identify life-history features-like survival, dispersal, and brood-failure rate-that promote redirected helping. The effect of these life-history features, in general, depends on which component of fitness (survival or fecundity) benefits from help and how brood failure varies among demes. Unlike previous authors, we find that helping can be more strongly promoted when it provides survival rather than fecundity benefits.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.609

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)0.0010.000

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 head0.017
GPT teacher head0.349
Teacher spread0.331 · 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 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
Published2024
Admission routes2
Has abstractyes

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