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The Relationship Between Resource Control, Association with Females and Male Weapon Size in a Male Dominance Insect

2006· article· en· W2094470830 on OpenAlexaff
Clint D. Kelly

Bibliographic record

VenueEthology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiologyDominance (genetics)MatingZoologyEcologySexual dimorphismDemography

Abstract

fetched live from OpenAlex

Abstract In species with a resource‐defence (male dominance) mating system, males are expected to maximize fitness by controlling resources deemed more valuable by sexually receptive females because these sites attract more mates. Furthermore, males, which control more valuable resources should themselves be of high quality. I experimentally tested these predictions in the laboratory using the sexually dimorphic Wellington tree weta, Hemideina crassidens (Blanchard) (Orthoptera: Tettigonioidea: Anostostomatidae). Male H. crassidens use their mandibular weaponry to fight for control of harems (groups of adult females) that seek shelter in trees cavities (galleries). As predicted, larger galleries housed significantly larger groups of females and males with larger weaponry controlled large galleries significantly more often. Therefore, galleries with a larger volume are likely considered more valuable by males because they house larger harems. However, contrary to prediction, males with larger weaponry did not reside with significantly more females overall because females did not always form the largest possible groups in galleries and males with smaller weaponry were able to reside with single females in small galleries. The latter observation suggests a possible alternative mating strategy by disadvantaged males.

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 score0.012
Threshold uncertainty score0.693

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)0.0000.000

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 head0.050
GPT teacher head0.229
Teacher spread0.179 · 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

Citations41
Published2006
Admission routes1
Has abstractyes

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