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Record W4413758821 · doi:10.1111/mms.70061

Seasonal Abundance and Distribution of Cetaceans in a High Traffic Shipping Corridor

2025· article· en· W4413758821 on OpenAlexafffundabout
Christie J. McMillan, Elise A. Keppel, Lisa D. Spaven, Stacey Hrushowy, Thomas Doniol‐Valcroze

Bibliographic record

VenueMarine Mammal Science · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsAbundance (ecology)Distribution (mathematics)GeographyEcologyFisheryBiologyMathematics

Abstract

fetched live from OpenAlex

ABSTRACT The recovery of many cetacean species coincides spatially and temporally with intensifying anthropogenic threats. We undertook multi‐year systematic surveys to quantify seasonal abundance and document the distribution of at‐risk cetaceans, which is needed to assess the impacts of increasing human activities in the Canadian portions of the southern Salish Sea and Swiftsure Bank. We completed 21 line‐transect surveys encompassing 5346 km of visual effort from September 2020 to December 2022 and collected 1514 sightings of five cetacean species using distance sampling protocols. Humpback whales, harbor porpoises, and Dall's porpoises were the most sighted species and were present in the area year‐round, with strong seasonal differences in their abundance and distribution. Estimated abundance of humpback whales was lowest in winter at 17 (95% CI: 11–26) and highest in fall at 416 (261–663). Harbor porpoise abundance was also lowest during winter at 606 (366–1006) and highest in fall at 1415 (975–2055). Dall's porpoise abundance was lowest in summer at 65 (38–112) and highest in winter at 333 (224–494). These estimates were not corrected for availability or perception bias; thus, they may underestimate true abundance to some extent. These seasonal patterns in abundance and distribution will inform threat assessment and mitigation for cetaceans in this area of high and increasing vessel traffic.

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 score0.124
Threshold uncertainty score0.451

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.002
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 head0.007
GPT teacher head0.225
Teacher spread0.217 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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