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Record W2949327040 · doi:10.4039/tce.2019.32

Natural variation in the growth and development of Protopiophila litigata (Diptera: Piophilidae) developing in three moose (Artiodactyla: Cervidae) antlers

2019· article· en· W2949327040 on OpenAlexaff
Christopher S. Angell, Olivia Cook

Bibliographic record

VenueThe Canadian Entomologist · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAntlerBiologyPupaZoologyEcologyLarva

Abstract

fetched live from OpenAlex

Abstract In animals, the early-life environment influences growth and development, which can have lasting effects on life history and fitness into adulthood. We investigated the patterns of growth, pupal development time, and their covariation in Protopiophila litigata Bonduriansky (Diptera: Piophilidae) larvae of both sexes collected from three discarded moose ( Alces alces (Linnaeus) (Artiodactyla: Cervidae)) antlers of varying size, chewing damage (used to infer relative age), and P. litigata density. Males tended to be smaller and their pupation lasted longer than females. One of the antlers was highly attractive to adult P. litigata , whereas the other two attracted few or none. Individuals from one antler of low attractiveness were smaller and took longer to eclose than individuals from either other antler, perhaps due to its high larval density. The relationship between body size and pupal development time also differed among antlers, being positively correlated in the most attractive antler and negatively correlated in the two other antlers.

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 score0.997
Threshold uncertainty score0.006

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 head0.027
GPT teacher head0.225
Teacher spread0.198 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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