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Record W2015301899 · doi:10.1139/z02-238

Seasonal change in egg-volume variation within a clutch in the Bull-headed Shrike, <i>Lanius bucephalus</i>

2003· article· en· W2015301899 on OpenAlexvenueno aff
Masaoki Takagi

Bibliographic record

VenueCanadian Journal of Zoology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyHatchingHatchlingShrikeAvian clutch sizeClutchAnimal scienceSeasonal breederEcologyReproductionZoologyHabitat

Abstract

fetched live from OpenAlex

I studied the seasonal change in egg volume in Bull-headed Shrikes, Lanius bucephalus, to explore the importance of egg-volume variation within a clutch. The mean egg volume did not change over a season; however, the coefficient of variation in egg volume in six-egg clutches significantly increased in 1994 and 1995 but did not change in 1996. Peaks in arthropod biomass occurred early in the breeding seasons in 1994 and 1995, but late in the season in 1996. Higher food availability was related to a reduction in variation in egg volume within a clutch. A significant difference in egg volume was found within six-egg clutches, and the first egg was the smallest. Nestlings that hatched from small eggs early in the hatching order suffered lower mortality rates than nestlings that hatched from large eggs laid late in the hatching order. The duration of time between settlement of a female in a male territory and clutch initiation decreased with date. Intraclutch variation in egg volume may be caused by a trade-off between the time to develop an egg and the volume of the first egg within a clutch. Because eggs that hatch first do not need to be large for the hatchling to survive, the Bull-headed Shrikes may advance the clutch-initiation date at the cost of reducing the volume of the first egg.

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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.022
GPT teacher head0.236
Teacher spread0.214 · 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

Citations10
Published2003
Admission routes1
Has abstractyes

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