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Record W2104017517 · doi:10.1139/z03-121

Correlates to survival of juvenile sea otters in Prince William Sound, Alaska, 19921993

2003· article· en· W2104017517 on OpenAlexvenueno aff
Brenda E. Ballachey, James L. Bodkin, Shay Howlin, Angela M. Doroff, Alan H. Rebar

Bibliographic record

VenueCanadian Journal of Zoology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersU.S. Fish and Wildlife Service
KeywordsBiologyJuvenileForagingPredationOtterMustelidaeSound (geography)Marine mammalZoologyFisheryEcologyOceanography

Abstract

fetched live from OpenAlex

We estimated survival of sea otters (Enhydra lutris) for 1 year post weaning during 1992–1993 in Prince William Sound (PWS), location of the 1989 Exxon Valdez oil spill. We sampled 38 pups in eastern PWS (EPWS), an unoiled area occupied by sea otters for <15 years, and 33 pups from oiled western PWS (WPWS), occupied for >25 years. We compared survival between areas, sexes, and condition groups. We also examined the relation of blood parameters to survival. Survival was estimated at 0.74 in EPWS and 0.52 in WPWS. Female survival was 0.86 in EPWS and 0.64 in WPWS, whereas male survival was lower, 0.61 in EPWS and 0.44 in WPWS. Sea otters from EPWS were in better condition (mass/length) than WPWS sea otters. Pups in better condition had higher survival in EPWS but not in WPWS. Foraging success was greater in EPWS than in WPWS, consistent with either an effect of length of occupation or the effects of oil on the prey base or a combination of these effects. Area differences in blood parameters suggested liver damage in WPWS sea otters, perhaps resulting from continued exposure to oil. Thus, both length of occupation and oiling history likely influenced juvenile survival in PWS.

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)0.001
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 score0.101
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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

Citations36
Published2003
Admission routes1
Has abstractyes

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