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Record W4385563929 · doi:10.7589/jwd-d-22-00172

CAUSES AND TRENDS OF HARBOR SEAL (PHOCA VITULINA) MORTALITY ALONG THE BRITISH COLUMBIA COAST, CANADA, 2012–2020

2023· article· en· W4385563929 on OpenAlexaffabout
Courtney Pace, Martin Haulena, Hannah E. Drumm, Lindsaye Akhurst, Stephen Raverty

Bibliographic record

VenueJournal of Wildlife Diseases · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMinistry of AgricultureGovernment of British ColumbiaVancouver Aquarium
Fundersnot available
KeywordsPhocaHarbor sealBiologyZoology

Abstract

fetched live from OpenAlex

A retrospective study was conducted to categorize and describe the causes of mortality in harbor seals (Phoca vitulina) along the British Columbia coast that presented to the Vancouver Aquarium Marine Mammal Rescue Centre (MMR) for rehabilitation from 2012 to 2020. Medical records for 1,279 predominantly perinatal live-stranded harbor seals recovered in this region were reviewed. Approximately 20.0% (256 individuals; 137 males, 118 females, 1 unknown) of these animals died while at MMR. Infectious disease was the most common cause of death, accounting for 60.5% of mortality across all age classes. This was followed by nonanthropogenic trauma (7.1%), metabolic illness (5.4%), nutritional deficiency (5.0%), parasitic illness (5.0%), congenital disorders (2.5%), and human-associated trauma (0.4%). Pups were the most common age class (87.4%) amongst mortalities and predominantly died of an infectious process (62.5%). Phocid herpesvirus-1 infection was identified in 18.9% of the mortalities, with the highest prevalence occurring in 2019 (30.8%). Fungal disease was detected in six seals: three cases of pulmonary mycosis due to Cryptococcus gattii and three cases consistent with mucormycosis. In six cases, mortality was attributed to congenital disorders. Two of these cases involved axial skeletal malformities that are not currently described in the literature. This is the first study to describe the causes of mortality in harbor seals undergoing rehabilitation in British Columbia.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.040
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2023
Admission routes2
Has abstractyes

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