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Record W4409683985 · doi:10.1111/conl.13102

Passive Acoustic Gliders Are Effective Monitoring Tools for Dynamic Management Plans Aimed at Mitigating Whale‐Vessel Strikes

2025· article· en· W4409683985 on OpenAlexafffundabout
Katherine L. Indeck, Michael F. Baumgartner, Laurence Lecavalier, Frederick G. Whoriskey, Kimberley T. A. Davies

Bibliographic record

VenueConservation Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsDalhousie UniversityOcean Tracking NetworkTransport CanadaUniversity of New Brunswick
FundersTransport Canada
KeywordsBusinessWhaleEnvironmental scienceEnvironmental resource managementFisheryEnvironmental planningBiology

Abstract

fetched live from OpenAlex

ABSTRACT Dynamic management is intended to mitigate the impacts of human activities on wildlife when and where the activities overlap with at‐risk species. Amid an ever‐growing maritime industry, we researched the performance of mobile underwater passive acoustic gliders as near real‐time monitoring assets for the purpose of whale‐vessel strike mitigation through dynamic management. Across 580 glider survey days, 30 near real‐time acoustic detections of critically endangered North Atlantic right whales (NARWs) triggered 194 days of mandatory 10‐knot vessel speed limits in three Canadian Dynamic Shipping Zones (DSZs). We found a high degree of interannual and seasonal variation in NARW acoustic occurrence and vessel slowdowns in the DSZs. Gliders were more effective than aerial surveillance at triggering slowdowns by a factor of 2–5 during fall and summer but were less effective during spring. Our results provide unambiguous evidence that gliders are effective monitoring platforms that can enhance dynamic ocean management goals globally.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.511
Threshold uncertainty score0.765

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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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 designBench or experimental
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

Citations4
Published2025
Admission routes3
Has abstractyes

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