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Record W6948441094 · doi:10.5061/dryad.c6g7d

Data from: Sex-biased dispersal is independent of sex ratio in a semiaquatic insect

2017· dataset· en· W6948441094 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2017
Typedataset
Languageen
FieldChemistry
TopicWood and Agarwood Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiological dispersalSex ratioMetapopulationCompetition (biology)PopulationPhilopatry

Abstract

fetched live from OpenAlex

Dispersal influences a variety of ecological and evolutionary dynamics including metapopulation persistence and local adaptation. Sex-biased dispersal evolves when the costs and benefits associated with dispersal differ between the sexes. These costs and benefits may be fixed, resulting in a consistent pattern of sex-biased dispersal within species whereby one sex always disperses more and/or further than the other. Alternatively, the costs and benefits may vary depending on the intensity of competition experienced by the two sexes. In this case, the direction of the sex bias may be plastic and depend on the sex ratio of the population. In the current study, we asked whether a semiaquatic, flight capable insect (Notonecta undulata) exhibits sex-biased dispersal and whether the strength of intrasexual competition experienced by males and females determines the direction of the sex bias. We conducted a mesocosm experiment in which we manipulated the population sex ratio and measured the probability of dispersal for males and females. We found that while both sexes dispersed, male dispersal rates were higher, and this pattern was independent of sex ratio. This suggests that fixed sex-specific dispersal costs and/or benefits are likely to be more important determinants of sex-biased dispersal in notonectids than population sex ratio.

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)0.004
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.055
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0550.051

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 head0.060
GPT teacher head0.320
Teacher spread0.260 · 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
GenreDataset

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
Published2017
Admission routes1
Has abstractyes

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