MétaCan
Menu
Back to cohort
Record W2922038502 · doi:10.1016/j.envint.2019.02.055

Mercury contents in rice and potential health risks across China

2019· article· en· W2922038502 on OpenAlexaff
Huifang Zhao, Haiyu Yan, Leiming Zhang, Guangyi Sun, Ping Li, Xinbin Feng

Bibliographic record

VenueEnvironment International · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsEnvironment and Climate Change Canada
FundersYouth Innovation Promotion Association of the Chinese Academy of SciencesChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsMercury (programming language)ChinaEnvironmental scienceEnvironmental chemistryEnvironmental healthHeavy metalsMERCURY EXPOSUREChemistryGeographyBiomonitoringMedicineComputer science

Abstract

fetched live from OpenAlex

Rice samples were collected at 560 sites in 15 provinces across China in areas without known point mercury (Hg) sources. Total mercury (THg) and methylmercury (MeHg) concentrations were analyzed in these rice samples for risk assessment. Relatively low THg and MeHg concentrations were found in the majority of the white rice samples with an overall mean of 4.74 (1.06–22.7) μg kg−1 and 0.682 (0.03–8.71) μg kg−1, respectively. The means (range of) THg concentration of rice in each geographical region were 5.23 (1.07–19.5) μg kg−1, 5.14 (1.06–17.2) μg kg−1, 4.45 (1.41–17.2) μg kg−1, 4.20 (1.48–19.4) μg kg−1, 3.49 (1.49–10.7) μg kg−1, and 4.53 (1.30–19.4) μg kg−1 in east, centre, south, southwest, northwest and northeast, China, respectively, and the corresponding values for MeHg concentrations were 0.898 (0.127–8.35) μg kg−1, 0.603 (0.207–2.48) μg kg−1, 0.516 (0.032–1.50) μg kg−1, 0.615 (0.050–5.03) μg kg−1, 0.704 (0.148–2.41) μg kg−1 and 0.565 (0.035–8.71) μg kg−1, respectively. Hg contents in rice across China were found to be at background levels. Both the probable daily intakes (PDIs) of inorganic Hg (IHg) and MeHg from rice consumption showed low risks for general population in the investigated regions.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

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)0.0040.001

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 head0.021
GPT teacher head0.300
Teacher spread0.279 · 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; both teacher heads agree on what is shown here.

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

Citations78
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueEnvironment InternationalSame topicMercury impact and mitigation studiesFrench-language works237,207