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Record W3209351821 · doi:10.1111/mms.12885

Post release monitoring of rehabilitated gray seal pups over large temporal and spatial scales

2021· article· en· W3209351821 on OpenAlexaff
Sue Sayer, Rebecca Allen, Katie Bellman, Marion Beaulieu, Tamara Cooper, Natalie Dyer, Kirsten Hockin, Kate Hockley, Dan Jarvis, Grace Jones, Paul Oaten, Natalie Waddington, Matthew J. Witt, Lucy A. Hawkes

Bibliographic record

VenueMarine Mammal Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsBamfield Marine Sciences Centre
Fundersnot available
KeywordsWildlifeBayWildlife managementBiological dispersalGray (unit)GeographyWildlife conservationWildlife refugeEcologyFisheryCartographyBiologyDemographyArchaeologyMedicine

Abstract

fetched live from OpenAlex

Abstract Wildlife rescue and rehabilitation is used globally to aid the conservation and welfare of marine species, however, postrelease monitoring is challenging. Here, long‐term, regional postrelease monitoring provides feedback for rehabilitation centers for gray seals ( Halichoerus grypus ). Data from 1,094 rehabilitated gray seals over 19 years across the southwest UK were examined to assess postrelease survivorship and the impact of release site on movements and range. Using flipper tags combined with photo identification, 391 rehabilitated seals (35.7%) were resighted, including 188 seals (17.2%) that were traced back to a specific rehabilitated individual with release data. The maximum monitoring duration for a single rehabilitated seal was 17 years, although the majority (151/188; 80%) were sighted for less than 5 years and 80/188 (43%) were resighted for less than a year. Almost all 188 traced rehabilitated seals ( n = 176, 93.6%) visited the St Ives Bay Wild Site, yet only half had been released at the adjacent St Ives Bay Release Site. Rehabilitated seals had similar dispersal patterns to their wild conspecifics but over a smaller area. Once released, rehabilitated animals face the same threats as their wild counterparts.

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)0.001
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 score0.048
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.239
Teacher spread0.230 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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