MétaCan
Menu
Back to cohort
Record W1428898108

Sources of γ-HCH in Chinese air at near-surface level and deposited to Chinese soil.

2009· article· en· W1428898108 on OpenAlexaff
Tian ChongGuo, Nanqi Ren, Jianmin Ma, LI Yi-fan

Bibliographic record

VenueChina Environmental Science · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Agricultural Sciences
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsChinaDeposition (geology)Environmental sciencePhysical geographyGeographySoil waterChineHydrology (agriculture)Environmental protectionGeologySoil scienceStructural basinArchaeologyGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

CanMETOP model was employed to assess the contributions to γ-HCH in Chinese air and soil from four major γ-HCH residual regions, including India, the former Soviet Union (FSU), Europe (excluding the FSU),and China itself in 2005. Modeled annual average air concentrations of γ-HCH at 1.5 m height above ground surface ranged for 10~100 pg/m3 in eastern region, and 1~10 pg/m3 in western region of China. The former was mainly due to Chinese source (30%~80%), while the major of the later was attributable to Indian source ( 50%). The European and FSU sources both mostly contributed to the Chinese northwestern region (10%). The contributions of deposition in China were also different in different area. Chinese source dominated the deposition of γ-HCH in the northeastern area (75%), while Indian source made the largest contribution in northwestern (63%) and southern (67%) areas of China. For whole China, annual total deposition of γ-HCH in 2005 was 691 t, among which, 55.1% was from India, 31.6% was from China, 3.6% was from Europe, and 2.5% was from the FSU.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.457

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueChina Environmental ScienceSame topicEnvironmental and Agricultural SciencesFrench-language works237,207