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Record W2169865051 · doi:10.1139/z05-104

When and how do predator starting distances affect flight initiation distances?

2005· article· en· W2169865051 on OpenAlexvenueno aff
W. E. Cooper

Bibliographic record

VenueCanadian Journal of Zoology · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsPredatorPredationEscape responseBiologyEcologyLizardGeneralityAffect (linguistics)ForageCommunication

Abstract

fetched live from OpenAlex

The distance separating predator and prey when the predator begins to approach, starting distance, was recently shown to affect flight initiation distance in many bird species, raising questions about the effect's generality, variation with ecological factors, and economic basis. I studied the effect in two lizard species that forage by ambush and escape into nearby refuges. Monitoring costs during approach are absent because ambushers remain immobile while scanning for prey and predators. Risks are minimized because of the proximity to refuge. Flight initiation distance increased weakly with starting distance in Sceloporus virgatus Smith, 1938 significantly only at rapid approach speed. It was not significant in Urosaurus ornatus (Baird and Girard, 1852) at slow approach speed. Flight initiation distance is predicted to increase with starting distance, owing to monitoring costs and assessment by prey of greater risk during prolonged approaches. The significant effect in S. virgatus, which lacks monitoring costs, is the first indication that risk affects the relationship between starting distance and flight initiation distance. Conditions in which starting distance is important and its possible effects in earlier studies are discussed, as well as standardizing approaches and possible artifactual effects of starting distance.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.216
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

Citations94
Published2005
Admission routes1
Has abstractyes

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