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Record W2995637312 · doi:10.3390/toxics7040065

North Pacific Baleen Whales as a Potential Source of Persistent Organic Pollutants (POPs) in the Diet of the Indigenous Peoples of the Eastern Arctic Coasts

2019· review· en· W2995637312 on OpenAlexaff
Pavel V. Chukmasov, Andrey Aksenov, Tatiana Sorokina, Yulia Varakina, Nikita Sobolev, Evert Nieboer

Bibliographic record

VenueToxics · 2019
Typereview
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMcMaster University
Fundersnot available
KeywordsArcticIndigenousBlubberPollutantBaleenBiomonitoringMarine mammalFisheryWhaleWhalingGeographyEnvironmental scienceBiologyEcology

Abstract

fetched live from OpenAlex

Among marine mammals, gray and bowhead whales contain large amounts of fat and thereby constitute crucial dietary components of the traditional diet of indigenous peoples of the Eastern Arctic. Despite the high nutritional and cultural value of gray and bowhead whales, there is a risk of persistent organic pollutant (POP) intake by indigenous individuals who use marine mammals as their main source of fat. POPs are lipophilic pollutants and are known to accumulate and magnify along the marine food web. Consumption of foods contaminated by POPs can perturb the endocrine, reproductive, and immune systems, and can potentially cause cancer. Moderate to relatively high concentrations of POPs have indeed been reported in the edible tissues of gray and bowhead whales consumed by indigenous peoples of the North Pacific Ocean. Even though their consumption is potentially harmful, there is no regular monitoring of eco-toxicants in the foods consumed by the indigenous peoples of the Eastern Arctic. In our view, the routine analyses of consumable parts of whales and of comparable nutritional items need to be included in the Russian Arctic Biomonitoring Programme.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.027
GPT teacher head0.243
Teacher spread0.216 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2019
Admission routes1
Has abstractyes

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