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Record W2796253663 · doi:10.1139/z03-079

Risk factors affecting escape behavior by the desert iguana, <i>Dipsosaurus dorsalis</i>: speed and directness of predator approach, degree of cover, direction of turning by a predator, and temperature

2003· article· en· W2796253663 on OpenAlexvenueno aff
W. E. Cooper

Bibliographic record

VenueCanadian Journal of Zoology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsnot available
Fundersnot available
KeywordsBurrowPredatorLizardIguanaPredationBiologyEscape responseEcologyIguanidaeSauriaZoology

Abstract

fetched live from OpenAlex

Escape from predators by desert iguanas (Dipsosaurus dorsalis) conforms to predictions of optimal escape theory based on risk. I simulated an approaching predator to study risk factors. The primary response variable was approach distance (= flight-initiation distance), i.e., the distance between predator and prey when the prey initiates escape. In additional studies, I recorded whether lizards permitted me to approach close enough to noose them (an indicator of wariness) and the method of escape. Approach distance was greater when the predator approached rapidly than slowly and directly than indirectly, and when the predator turned toward the lizard rather than away. It was greater in open than in more densely covered habitats, which may reflect greater risk due to conspicuousness and (or) a greater distance to refuge. Early in the day at lower air temperatures, desert iguanas permitted a closer approach before initiating escape. While basking after emergence from burrows, lizards escaped into burrows; later in the day they fled. Lizards that fled had high body temperatures; a single individual captured immediately after entering a burrow had a lower body temperature. Lizards presumably enter burrows when low body temperature limits the running speed, but burrow use is costly because attainment of the activity temperature is delayed because of time elapsed and the temperature decrease in burrows.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.021
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.201
Teacher spread0.191 · 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 teacher head, 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

Citations96
Published2003
Admission routes1
Has abstractyes

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