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Record W2147317841 · doi:10.1139/z02-151

Effect of larval body size on overwinter survival and emerging adult size in the burying beetle, <i>Nicrophorus investigator</i>

2002· article· en· W2147317841 on OpenAlexvenueno aff
R. Smith

Bibliographic record

VenueCanadian Journal of Zoology · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyFecundityLarvaOverwinteringLongevityEcologyZoologyReproductive successPopulationDemography

Abstract

fetched live from OpenAlex

Body size may influence both adult fecundity and the probability of survival through each life stage. Previous studies of burying beetles (Nicrophorus, Coleoptera: Silphidae) have revealed reproductive advantages for larger adults and the role of parental care in determining larval size and number. In this study I measure the effect of size on survival over the winter period and the correlation between larval size and the size of emerging adults. I collected data from 24 groups of 20–25 larvae sorted by size and overwintered outside under natural conditions in Colorado, U.S.A. There was a significant positive correlation between larval size and adult size at emergence and a significant effect of size on overwinter survival. Data from 2 years yielded the following mean survival rates: small, 47.3 ± 1.0%; medium, 73.2 ± 0.7%; large, 85.7 ± 0.4%. These values were then used to accurately predict adult emergence from broods of larvae whose range of size was measured prior to the overwinter period. The results indicate that selection for large body size may result from an overwinter survival advantage and not just from reproductive success. This has implications for fitness models of parental care and models of population dynamics.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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

Citations43
Published2002
Admission routes1
Has abstractyes

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