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Record W1903492469 · doi:10.1111/mms.12250

An evaluation of active acoustic methods for detection of marine mammals in the Canadian Beaufort Sea

2015· article· en· W1903492469 on OpenAlexafffundabout
Cynthia D. Pyć, Maxime Geoffroy, Frank Reier Knudsen

Bibliographic record

VenueMarine Mammal Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversité LavalBP (Canada)
FundersArcticNetAurora Research InstituteBP Exploration Operating Company Limited
KeywordsBeluga WhaleBeaufort seaMarine mammals and sonarMarine mammalLeucasSonarBelugaBeaufort scaleFisheryCetaceaOceanographySound (geography)ArcticEnvironmental scienceGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Abstract A commercially available fisheries sonar was mounted on an icebreaker and evaluated during an environmental baseline study in the Canadian Beaufort Sea, to determine the applicability of active acoustic monitoring ( AAM ) for marine mammal detection by comparing marine mammal observer ( MMO ) visual sightings and active acoustic detections. During 170 h of simultaneous MMO and AAM , 115 bowhead whales ( Balaena mysticetus ) and four beluga whales ( Delphinapterus leucas ) were visually sighted by MMO s, while 59 sonar detections of bowhead whales occurred using AAM . The fisheries sonar detected 92% of the cetaceans observed within 2,000 m. Additional observations of ringed seals ( Pusa hispida ) and bearded seals ( Erignathus barbatus ) were recorded both by MMO s and AAM . Comparative results indicate that a commercially available active acoustic system can consistently detect marine mammals within varying ranges dictated by water column properties. Shallow environments and strong pycnoclines currently present challenges to AAM .

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)0.012
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.069
GPT teacher head0.367
Teacher spread0.298 · 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

Citations9
Published2015
Admission routes3
Has abstractyes

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