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Record W2134695704 · doi:10.1002/etc.2883

Increase in mercury in Pacific yellowfin tuna

2015· article· en· W2134695704 on OpenAlexfundno aff
Paul E. Drevnick, Carl H. Lamborg, Martin J. Horgan

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesNational Science Foundation
KeywordsMercury (programming language)Yellowfin tunaTunaEnvironmental scienceFisheryOceanographyEnvironmental chemistryBiologyFish <Actinopterygii>ChemistryGeology

Abstract

fetched live from OpenAlex

Mercury is a toxic trace metal that can accumulate to levels that threaten human and environmental health. Models and empirical data suggest that humans are responsible for a great deal of the mercury actively cycling in the environment at present. Thus, one might predict that the concentration of mercury in fish should have increased dramatically since the Industrial Revolution. Evidence in support of this hypothesis has been hard to find, however, and some studies have suggested that analyses of fish show no change in mercury concentration. By compiling and re-analyzing published reports on yellowfin tuna (Thunnus albacares) caught near Hawaii (USA) over the past half century, the authors found that the concentration of mercury in these fish currently is increasing at a rate of at least 3.8% per year. This rate of increase is consistent with a model of anthropogenic forcing on the mercury cycle in the North Pacific Ocean and suggests that fish mercury concentrations are keeping pace with current loading increases to the ocean. Future increases in mercury in yellowfin tuna and other fishes can be avoided by reductions in atmospheric mercury emissions from point sources.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.090
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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)0.0020.000

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 head0.012
GPT teacher head0.228
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 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

Citations74
Published2015
Admission routes1
Has abstractyes

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