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GROWTH AND DEVELOPMENT IN FREE‐RANGING HARBOR SEAL (<i>PHOCA VITULINA</i>) PUPS FROM SOUTHERN BRITISH COLUMBIA, CANADA

2002· article· en· W2026045980 on OpenAlexafffundabout
Paul Cottrell, Steven Jeffries, Brian Beck, Peter S. Ross

Bibliographic record

VenueMarine Mammal Science · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsFisheries and Oceans Canada
FundersDivision of Ocean SciencesFisheries and Oceans Canada
KeywordsPhocaWeaningHarbor sealBiologyAnimal scienceFishery

Abstract

fetched live from OpenAlex

Abstract Harbor seals (Phoca vitulina) are small pinnipeds that are widely distributed throughout the temperate coastal regions of the Atlantic and Pacific oceans. We determined birth mass, neonatal growth rates, weaning age, and weaning mass of NE Pacific harbor seals (P. v. richardsi) during a capture‐recapture study that spanned the nursing period (Sidney Island, British Columbia, Canada). Of 46 harbor seal pups initially captured, 28 were classified as newborns (i. e., < 24 h old). Mean body mass of newborns was 11.2 ± SE 0.31 kg. Pups were individually tagged and recaptured throughout the nursing period. Average daily mass gain during the nursing period was 394 ± 26 g. Mean birth mass of males did not differ significantly from females, although pups found with fetal pelage (lanugo) (21.4% of all newborns) were smaller at birth (9.8 ± 0.44 kg) than non‐lanugo pups (11.6 ± 0.33 kg). Mean weaning mass was estimated at 23.6 ± 1.2 kg at a mean weaning age of 32 d ± 1.5 d. While birth and weaning masses differed little from the published data for offshore Sable Island harbor seals (P. v. concolor), British Columbia harbor seals are characterized by half the daily mass gain and a longer nursing period.

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)0.000
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 score0.141
Threshold uncertainty score0.285

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.007
GPT teacher head0.160
Teacher spread0.153 · 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

Citations68
Published2002
Admission routes3
Has abstractyes

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