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Assessing changes in numbers and distribution of large whale entanglements in Newfoundland and Labrador, Canada<sup>1</sup>

2011· article· en· W1989551406 on OpenAlexaboutno aff
Steven Benjamins, Wayne Ledwell, Julie Huntington, Anthony R. Davidson

Bibliographic record

VenueMarine Mammal Science · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersNatural Environment Research CouncilSight Research UK
KeywordsMinke whaleFisheryBalaenopteraHumpback whaleWhalingWhaleGadusFishingGeographyAtlantic codCetaceaOceanographyFish <Actinopterygii>BiologyGeology

Abstract

fetched live from OpenAlex

Abstract Entanglements of large whales in commercial fisheries in Newfoundland and Labrador, Canada, have been consistently recorded since 1979, as part of a program aimed at releasing captured animals and reducing costs to fishermen. This data set represented an opportunity to identify fisheries posing particular entanglement risks to local whale populations. Data were assessed over the periods 1979–1992 and 1993–2008, corresponding to distinct phases in fisheries distribution and intensity. Between 1979 and 2008, 1,209 large whale entanglements were recorded in Newfoundland and Labrador. These were mostly humpback whales ( Megaptera novaeangliae ; 80%) and minke whales ( Balaenoptera acutorostrata ; 15%). Dramatic declines in reported inshore whale entanglement rates were observed following the 1992 moratorium on Atlantic cod ( Gadus morhua ) fisheries. Recently, more entanglements have been reported further offshore, largely due to expansion of fisheries targeting snow crab ( Chionoecetes opilio ). For all whale species, entanglement rates and associated mortality rates varied considerably in different fishing gear. Fractions of humpback and minke whales found dead in different fishing gear differed substantially, with minke whales far more likely to be found dead than humpback whales.

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 score0.047
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.242
Teacher spread0.224 · 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

Citations33
Published2011
Admission routes1
Has abstractyes

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