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Record W2065507950 · doi:10.1021/es403651x

Fish Mercury Levels Appear to Be Increasing Lately: A Report from 40 Years of Monitoring in the Province of Ontario, Canada

2014· article· en· W2065507950 on OpenAlexaffabout
Nilima Gandhi, Rex W.K. Tang, Satyendra P. Bhavsar, George B. Arhonditsis

Bibliographic record

VenueEnvironmental Science & Technology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsMinistry of the Environment, Conservation and ParksUniversity of Toronto
Fundersnot available
KeywordsMercury (programming language)Fish <Actinopterygii>Environmental protectionFisheryEnvironmental scienceGeographyBiologyComputer science

Abstract

fetched live from OpenAlex

Recent mercury levels and trends reported for North America suggest a mixed (positive/negative) outlook for the environmental mercury problem. Using one of the largest consistent monitoring data sets in the world, here we present long-term and recent mercury trends in Walleye, Northern Pike, and Lake Trout from the Province of Ontario, Canada, which contains about one-third of the world's fresh water and covers a wide geographical area (1.5 and 3 times larger than France and Germany, respectively). Overall, the results indicate that the fish mercury levels either declined (0.01-0.07 μg/g decade) or remained stable between the 1970s and 2012. The rates of mercury decline were substantially greater (mostly 0.05-0.31 μg/g decade) during the 1970s/80s possibly in response to reductions in mercury emissions. However, Walleye and Pike levels have generally increased (0.01-0.27 μg/g decade) in recent years (1995-2012), especially for northern Ontario (effect sizes for differences between the two periods ranged from 0.39 to 1.04). Proportions of Walleye and Pike locations showing a flat or increasing trend increased from 26-44% to 59-73% between the 1970s/80s and 1995-2012. Mercury emissions in North America have declined over the last few decades, and as such it is logical to expect recovery in fish mercury levels; however, other factors such as global emissions, climate change, invasive species, and local geochemistry are likely affecting the response time and magnitude.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.013
GPT teacher head0.229
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 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

Citations83
Published2014
Admission routes2
Has abstractyes

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