MétaCan
Menu
Back to cohort
Record W2055026462 · doi:10.14430/arctic378

Potential Sources of Monomethyl Mercury in Arctic and Subarctic Seawater

2009· article· en· W2055026462 on OpenAlexvenueaboutno aff
Jane L. Kirk

Bibliographic record

VenueARCTIC · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsMercury (programming language)ArcticEnvironmental scienceArctic ecologyEnvironmental chemistrySeawaterEcosystemOceanographyMarine ecosystemEcologyGeologyChemistry

Abstract

fetched live from OpenAlex

(Hg) IS A HEAVY METAL that exists in many different forms in the environment. One of these forms, monomethyl mercury (MMHg), is a strong vertebrate neurotoxin that bioaccumulates through food webs. In some Canadian Arctic marine mammals, MMHg has increased to levels that may be toxic to northern peoples who consume these species as traditional foods (INAC, 2003). Unfortunately, it is difficult to determine the sources of Hg contamination in northern food webs because the cycling of Hg is complex and not well characterized in Arctic marine ecosystems. Mercury may enter the Arctic via a number of pathways (see general schematic). For example, gaseous Hg(0) released by industrial processes such as coal combustion and waste incineration is relatively stable in the atmosphere and can be transported to the Arctic on air currents (Shroeder and Munthe, 1998). Gaseous Hg(0) can flux into or out of waterbodies, such as lakes and oceans, depending on where concentrations of Hg(0) are higher. Inorganic Hg(II) and MMHg may also enter Arctic marine ecosystems in precipitation, river inflow, and glacial melt, as well as via ocean currents.

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 score0.126
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.010
GPT teacher head0.234
Teacher spread0.225 · 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

Citations8
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueARCTICSame topicMercury impact and mitigation studiesFrench-language works237,207