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Record W4392197194 · doi:10.1111/mms.13114

Effects of external tags on maternal postpartum, offspring body mass and breeding frequency in gray seals <i>Halichoerus grypus</i>

2024· article· en· W4392197194 on OpenAlexafffundabout
Charity C. Justrabo, Cornelia E. den Heyer, W. Don Bowen, Damian C. Lidgard

Bibliographic record

VenueMarine Mammal Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans CanadaUniversity of British ColumbiaDalhousie University
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaParks Canada
KeywordsOffspringWeaningReproductionBiologySeasonal breederNova scotiaAnimal scienceDemographyEcologyGeographyPregnancy

Abstract

fetched live from OpenAlex

Abstract Few studies have examined the impacts of externally fitted data‐loggers and telemetry tags on pinnipeds. We tested for instrument effects on body mass of lactating female gray seals and their offspring and probability of pupping in the next breeding season. Known‐age adult females ( n = 216) were fitted with instruments in winter, spring, and fall from 1992 to 2018 at Sable Island, Nova Scotia. Of those tagged in spring and fall, 61 of 135 returning females and 59 of their offspring were weighed within 5 days postpartum and 79 pups were weighed at weaning. Instrumented females were assigned to treatments based on tag frontal area sums, tag mass, deployment duration, and acoustic tag presence compared to control females without instruments using linear mixed‐effects models. None of the treatment effects were included in the preferred models predicting birth mass of offspring or probability of breeding in the following year. The small negative effect (−3% to −7%) on postpartum maternal mass and pup weaning mass (−4.7%) for females instrumented in fall may be an artifact as longer spring deployments showed no effect. Overall, we found that the instruments deployed had no detectable negative effects on the maternal and offspring variables measured.

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)0.001
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 score0.194
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.002
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 head0.007
GPT teacher head0.228
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 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

Citations1
Published2024
Admission routes3
Has abstractyes

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