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Record W3137757793 · doi:10.1021/acs.est.0c08563

Individual Prey Specialization Drives PCBs in Icelandic Killer Whales

2021· article· en· W3137757793 on OpenAlexafffund
Anaïs Remili, Robert J. Letcher, Filipa I. P. Samarra, Runé Dietz, Christian Sonne, Jean‐Pierre Desforges, Gísli A. Víkingsson, David A. Blair, Melissa A. McKinney

Bibliographic record

VenueEnvironmental Science & Technology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsCarleton UniversityEnvironment and Climate Change CanadaMcGill University
FundersEnvironment and Climate Change CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsIcelandicPredationFisheryBiologyZoologyEcologyEnvironmental science

Abstract

fetched live from OpenAlex

Abstract Interindividual variation in prey specialization is an essential yet overlooked aspect of wildlife feeding ecology, especially as it relates to intrapopulation variation in exposure to toxic contaminants. Here, we assessed blubber concentrations of an extensive suite of persistent organic pollutants in Icelandic killer whales (Orcinus orca). Polychlorinated biphenyl (PCB) concentrations in blubber were >300-fold higher in the most contaminated individual relative to the least contaminated, ranging from 1.3 to 428.6 mg·kg–1 lw. Mean PCB concentrations were 6-to-9-fold greater in individuals with a mixed diet including marine mammals than in fish specialist individuals, whereas males showed PCB concentrations 4-fold higher than females. Given PCBs have been identified as potentially impacting killer whale population growth, and levels in mixed feeders specifically exceeded known thresholds, the ecology of individuals must be recognized to accurately forecast how contaminants may threaten the long-term persistence of the world’s ultimate marine predator.

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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Citations33
Published2021
Admission routes2
Has abstractyes

Explore more

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