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Record W4317033812 · doi:10.1111/mms.12997

Fly with care: belugas show evasive responses to low altitude drone flights

2023· article· en· W4317033812 on OpenAlexafffund
Jaclyn A. Aubin, Marie‐Ana Mikus, Robert Michaud, Daniel J. Mennill, Valeria Vergara

Bibliographic record

VenueMarine Mammal Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsRaincoast Conservation FoundationUniversity of Windsor
FundersKenneth M. Molson FoundationDonner Canadian FoundationParks CanadaNatural Sciences and Engineering Research Council of CanadaMolson Foundation
KeywordsDroneDisturbance (geology)Altitude (triangle)GeographyAeronauticsCetaceaEcologyBiologyEngineering

Abstract

fetched live from OpenAlex

Abstract Drones have become an important research tool for studies of cetaceans, providing valuable insights into their ecology and behavior. However, drones are also recognized as a potential source of disturbance to cetaceans, particularly when flown at low altitudes. In this study, we examined the impact of drones on endangered St. Lawrence belugas ( Delphinapterus leucas ), and reviewed drone studies of cetaceans to identify altitude thresholds linked to disturbance. We repurposed drone footage of free‐living belugas taken at various altitudes, speeds, and angles‐of‐approach, and noted the animals' reactions. Evasive reactions to the drone occurred during 4.3% (22/511) of focal group follows. Belugas were more likely to display sudden dives during low‐altitude flights, particularly flights below 23 m. Sudden dives were also more likely to occur in larger groups and were especially common when a drone first approached a group. We recommend that researchers maintain a lower altitude limit of 25 m in drone‐assisted studies of belugas and approach larger groups with caution. This recommendation is in line with our literature review, which indicates that drone flights above 30 m are unlikely to provoke disturbance among cetaceans.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.005
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.005

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 head0.012
GPT teacher head0.246
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

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

Citations26
Published2023
Admission routes2
Has abstractyes

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