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Record W1983559535 · doi:10.1021/es034157d

Global Gridded Emission Inventories of β-Hexachlorocyclohexane

2003· article· en· W1983559535 on OpenAlexaff
Yi-Fan Li, M. T. Scholtz, Bill J. Van Heyst

Bibliographic record

VenueEnvironmental Science & Technology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of GuelphEnvironment and Climate Change Canada
Fundersnot available
KeywordsHexachlorocyclohexaneEnvironmental scienceLindaneEnvironmental chemistryBiotaPersistent organic pollutantAtmosphere (unit)ContaminationTonneEnvironmental engineeringPesticideMeteorologyChemistryGeographyEcology

Abstract

fetched live from OpenAlex

As the contamination of beta-hexachlorocyclohexane (beta-HCH) in the Arctic air, seawater, and biota is an environmental concern, there is a need for emission inventories of beta-HCH to be used by the modeling community to predict the fate and transport of beta-HCH. This paper presents such emission inventories for beta-HCH. The total global usage of beta-HCH between 1945 and 2000 is estimated at 850 kt, 230 kt of which was emitted to the atmosphere over the same time period. Usage of beta-HCH was estimated to be around 36 kt in 1980 and 7.4 kt in 1990. Total beta-HCH emissions in 1980 were 9.8 kt with 83% attributed to the application in 1980 and 17% to soil residues due to prior applications. Total beta-HCH emissions in 1990 were 2.4 kt with 78% attributed to the application in 1990 and 22% to soil residues. While it assumed that no usage of technical HCH occurred in 2000, the global beta-HCH emissions in this year due to soil residues were estimated at 66 t. It has shown that the global beta-HCH emissions have undergone a "southward tilt" over the time period studied as more northern countries have banned the use of technical HCH.

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 score0.036
Threshold uncertainty score0.071

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.219
Teacher spread0.214 · 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

Citations107
Published2003
Admission routes1
Has abstractyes

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