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Record W2749596435 · doi:10.1111/eth.12642

Behavioural acclimation to cameras and observers in coral reef fishes

2017· article· en· W2749596435 on OpenAlexafffund
Gerrit B. Nanninga, Isabelle M. Côté, Ricardo Beldade, Suzanne C. Mills

Bibliographic record

VenueEthology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la RechercheDeutsche Forschungsgemeinschaft
KeywordsAcclimatizationEscape responseEcologyBiologyVigilance (psychology)Observer (physics)Coral reef

Abstract

fetched live from OpenAlex

Abstract Observer presence can bias behavioural studies of animals in both the wild and the laboratory. Despite existing evidence for significant observer effects across several taxa, little is known about the minimum periods of acclimation that should precede behavioural observations. To date, most studies either do not report any acclimation periods or include a non‐specific period without empirically quantifying its appropriateness. Here, we conducted in situ behavioural observations of two species of demersal coral reef fishes using cameras and/or observers to examine the biases associated with either approach. For both treatments, we generated 25 min time series of a number of vigilance‐associated behaviours (i.e., distance from shelter and mate, time out of shelter, swimming activity) and estimated the point of acclimation using changepoint analysis. In the camera trials, acclimation in both species appeared to occur between 2 and 7 min for different behaviours. When an observer was present, however, no apparent acclimation occurred until the observer left the area. Overall, our findings demonstrate that (i) behavioural studies of wild fishes conducted by an observer may be biased due to permanent observer effects, and (ii) when using video equipment, a species‐ and behaviour‐specific acclimation period should precede behavioural scoring.

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)0.000
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 score0.015
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.053
GPT teacher head0.297
Teacher spread0.244 · 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

Citations43
Published2017
Admission routes2
Has abstractyes

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