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Record W4320855927 · doi:10.1007/s13280-023-01831-6

Our evolved understanding of the human health risks of mercury

2023· review· en· W4320855927 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAMBIO · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsMcGill University
FundersNational Institute of Environmental Health SciencesDartmouth CollegeWorld Health Organization
KeywordsMercury (programming language)ConventionThe arcticEnvironmental healthMERCURY EXPOSUREHuman healthMercury pollutionOperationalizationGeographyEnvironmental protectionEnvironmental planningPollutionPolitical scienceEcologyLawMedicineBiologyOceanography

Abstract

fetched live from OpenAlex

Mercury (Hg) is a chemical of health concern worldwide that is now being acted upon through the Minamata Convention. Operationalizing the Convention and tracking its effectiveness requires empathy of the diversity and variation of mercury exposure and risk in populations worldwide. As part of the health plenary for the 15th International Conference on Mercury as a Global Pollutant (ICMGP), this review paper details how scientific understandings have evolved over time, from tragic poisoning events in the mid-twentieth century to important epidemiological studies in the late-twentieth century in the Seychelles and Faroe Islands, the Arctic and Amazon. Entering the twenty-first century, studies on diverse source-exposure scenarios (e.g., ASGM, amalgams, contaminated sites, cosmetics, electronic waste) from across global regions have expanded understandings and exemplified the need to consider socio-environmental variables and local contexts when conducting health studies. We conclude with perspectives on next steps for mercury health research in the post-Minamata Convention era.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.729
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.467
GPT teacher head0.465
Teacher spread0.002 · 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