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Record W2101448560 · doi:10.1017/s0952836903004047

Maternal and newborn life‐history traits during periods of contrasting population trends: implications for explaining the decline of harbour seals (<i>Phoca vitulina</i>), on Sable Island

2003· article· en· W2101448560 on OpenAlexaffabout
W. Don Bowen, Sara L. Ellis, Sara J. Iverson, Daryl J. Boness

Bibliographic record

VenueJournal of Zoology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsDalhousie UniversityBedford Institute of OceanographyFisheries and Oceans Canada
Fundersnot available
KeywordsPhocaBiologyPopulationJuvenileWeaningCompetition (biology)FecundityDemographyZoologyEcologyAnimal science

Abstract

fetched live from OpenAlex

Abstract Annual censuses of the number of harbour seal Phoca vitulina pups born on Sable Island Canada showed an increasing trend during the 1980s, but a rapid decline through the 1990s from 625 pups in 1989 to only 32 by 1997. Weekly surveys of the North Beach of the island during the 1991–98 breeding seasons showed that the number of adults and juveniles also declined during the 1990s. Despite the dramatic demographic changes, maternal postpartum mass, pup birth mass, relative birth mass, lactation duration, pup weaning mass and relative weaning mass showed no significant trends during 1987–96. However, two traits did change. The age structure of parturient females increased significantly, indicating reduced recruitment to the breeding population. Mean birth date increased by 7 days during the early 1990s, suggesting nutritional stress of females and later implantation dates. This nutritional stress may in turn have been caused by increased competition from the rapidly increasing grey seal population on Sable Island. Although minimum estimates of shark‐inflicted mortality can account for much of the decline, evidence suggests that food shortage arising from interspecific competition may have also played a role in causing the decline of the population through effects on fecundity and juvenile survival.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.261

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)0.0000.000

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 head0.025
GPT teacher head0.255
Teacher spread0.231 · 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

Citations116
Published2003
Admission routes2
Has abstractyes

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