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Shark‐inflicted mortality on a population of harbour seals (<i>Phoca vitulina</i>) at Sable Island, Nova Scotia

2000· article· en· W2127313361 on OpenAlexaffabout
Zoe Lucas, Wayne T. Stobo

Bibliographic record

VenueJournal of Zoology · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsBedford Institute of Oceanography
Fundersnot available
KeywordsPhocaNova scotiaBiologyPopulationReproductionZoologyDemographyEcologyGeography

Abstract

fetched live from OpenAlex

Abstract Shark‐inflicted mortality on harbour seals Phoca vitulina on Sable Island, Nova Scotia, was studied from 1980 to 1997, based on carcasses washed up on shore. During this period, pup production declined dramatically from over 600 in 1989 to 40 in 1997. Between 1980 and 1992, pup deaths only were recorded, and only during the May–June pupping period, while deaths in all age groups were recorded year‐round between 1993 and 1997; 458 pups, 23 juveniles and 241 adults were found. Shark‐inflicted mortality in pups, as a proportion of total production, was under 10% during 1980–93, roughly 25% in 1994–95, and increased to 45% in 1996. Shark‐inflicted mortality occurred in all months except December, January and February, with c. 80% of the pups killed during the pupping period, and 97% of the adults killed outside the pupping period. The decline in pup production was not only a result of reduced recruitment owing to pup mortality. A greater proportion of reproductive females than males was killed. We estimate that shark‐inflicted mortality on pups and adult females reduced pup production on Sable Island by 43 to 154 pups annually between 1993 and 1997. Our results indicate that sharks are having an impact on Sable Island harbour seals, possibly to the extent of limiting population growth, or contributing to the observed population decline. Potential reasons for this increased mortality are discussed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.989

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)0.0270.000

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 head0.021
GPT teacher head0.268
Teacher spread0.247 · 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.

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

Citations88
Published2000
Admission routes2
Has abstractyes

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