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Record W4280495914 · doi:10.1578/am.48.3.2022.234

Variability in Body Condition and Growth Rates for Rehabilitated Harbor Seal (Phoca vitulina) Pups

2022· article· en· W4280495914 on OpenAlexaboutno aff
Sarah J. Teman, Denise J. Greig, Sarah Wilkin, Joseph K. Gaydos

Bibliographic record

VenueAquatic Mammals · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsPhocaHarbor sealCondition indexBiologyBody weightWeaningSeal (emblem)MorphometricsAnimal scienceDemographyZoologyFisheryGeographyEndocrinology

Abstract

fetched live from OpenAlex

In the United States, Canada, and Europe harbor seal (Phoca vitulina) pups are commonly rehabilitated after stranding and then released. Size at release is likely important to post-release survival; however, data have not been compiled to track the body condition of rehabilitated harbor seals at release across the U.S. To better understand spatiotemporal variations in harbor seal morphometrics during rehabilitation and at release, this study retrospectively analyzed body conditions, weights, lengths, and growth rates of rehabilitated harbor seal pups in the U.S. Body condition index (BCI) was calculated, and weight and BCI were modeled regionally and temporally. There was significant variation in weight, length, BCI, and growth rate for rehabilitated and released seals between the East and West Coasts of the U.S. and among different years. Growth rates during rehabilitation were slower than reported for wild pups from birth to weaning. Length at release was not a strong predictor of weight. Because animals of similar weights can have different lengths, weight alone might not be the best criterion for pre-release body condition. A body condition score incorporating weight, length, and possibly other variables such as age or axillary girth could be more informative; however, data on post-release survival are needed to evaluate these options.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.028
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.263
Teacher spread0.251 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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