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Record W1990029044 · doi:10.1080/19393210.2012.683881

Determination of methylmercury in marine fish from coastal areas of Zhejiang, China

2012· article· en· W1990029044 on OpenAlexfundno aff
Zhu Huang, Jianlong Han, Pinggu Wu, Jun Tang, Ying Tan

Bibliographic record

VenueFood Additives and Contaminants Part B · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersNational Research Council CanadaCenters for Disease Control and PreventionChinese Center for Disease Control and Prevention
KeywordsMethylmercuryMarine fishFisheryChinaFish <Actinopterygii>GeographyEnvironmental scienceOceanographyEnvironmental protectionEcologyBiologyArchaeologyGeologyBioaccumulation

Abstract

fetched live from OpenAlex

The concentration of methylmercury (MMC) and total mercury (TMC) in marine fishes (five species) frequently consumed in the coastal areas of Zhejiang province, China, was determined. The method of high-performance liquid chromatography-atomic fluorescence spectrometry (HPLC-AFS) with the microwave-assisted extraction was used for the MMC determination. TMC was analysed by a direct mercury analyser. MMC and TMC concentrations in five fish species ranged from 53 to 158 µg kg⁻¹ and 60 to 172 µg kg⁻¹, respectively. The proportion of MMC levels in TMC was greater than 80%. The highest MMC and TMC levels were found in Hairtail.

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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.248
Teacher spread0.232 · 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

Citations20
Published2012
Admission routes1
Has abstractyes

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Same venueFood Additives and Contaminants Part BSame topicMercury impact and mitigation studiesFrench-language works237,207