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Record W2120061882 · doi:10.4039/ent133311-3

Sexual size dimorphism in a natural population of <i>Callicorixa vulnerata</i> (Hemiptera: Corixidae)

2001· article· en· W2120061882 on OpenAlexaff
Patrik Nosil

Bibliographic record

VenueThe Canadian Entomologist · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSexual dimorphismBiologyFecundityZoologyPopulationNatural selectionSexual selectionGerridaeTraitEcologyHemipteraDemography

Abstract

fetched live from OpenAlex

Sexual size dimorphism occurs in many species. Differences between males and females, in size or other characteristics, may result from sexual selection, fecundity selection, natural selection, non-adaptive processes, or a combination of these pressures (Darwin 1874; Selander 1966; Trivers 1976; Slatkin 1984; Shine 1989). In insects, females with large body size often produce more eggs than smaller females, and femalebiased sexual size dimorphism is commonly attributed to such fecundity selection (e.g., Preziosi and Fairbairn 1997; but see Leather 1988). Water boatmen are detrivorous or zoophagous aquatic insects often inhabiting small ponds of the Northern Hemisphere (Hungerford 1948; Nosil and Reimchen 2001). Female water boatmen are generally larger than males. In this note, I quantify the nature and magnitude of a previously undescribed sexual size dimorphism in a natural population of the water boatman Callicorixa vulnerata Uhler (Hemiptera: Corixidae). I tested for differences between males and females in mean trait size (body length, body weight, mid-leg tarsal length, mid-leg tarsal spine number), and also tested for sexual dimorphism in allometric relationships between tarsal traits and body length.

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)0.001
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.031
GPT teacher head0.217
Teacher spread0.186 · 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

Citations4
Published2001
Admission routes1
Has abstractyes

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