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Mutual mate choice by mountain pine beetles: size‐dependence but not size‐assortative mating

2010· article· en· W2058999203 on OpenAlexaff
Mary L. Reid, ORIT BARUCH

Bibliographic record

VenueEcological Entomology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiologyAssortative matingMate choiceMatingCurculionidaeSexual selectionEcologyZoologyReproduction

Abstract

fetched live from OpenAlex

1. Mutual mate choice may be rare, occurring when both sexes invest heavily in reproduction, mating opportunities are abundant, and individuals differ in quality. 2. Mountain pine beetles, Dendroctonus ponderosae (Curculionidae: Scolytinae) appear to meet the conditions for mutual mate choice. We introduced males to females in breeding sites and observed the occurrence and speed of a male entering a female's gallery. We tested for consequences of mutual mate choice, namely condition‐dependent choosiness and assortative mating. 3. Males were more likely to enter a female's gallery when the gallery was in a smaller tree with less resin production and when the gallery was larger. Female body size and condition did not influence the probability of entry. Larger males were less likely to enter a gallery than were smaller males, probably because of size‐dependent choosiness rather than physical limitations. 4. Small males took longer to enter galleries of large females than of small females, whereas large males entered as quickly into galleries of large females as small females. This suggests size‐dependent choosiness by females. 5. No assortative mating by body size was detected, probably because males appeared to choose on the basis of female‐associated resources rather than on female traits.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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 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

Citations19
Published2010
Admission routes1
Has abstractyes

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