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Record W1993269547 · doi:10.1021/es011142b

Chlordane Enantiomers and Temporal Trends of Chlordane Isomers in Arctic Air

2002· article· en· W1993269547 on OpenAlexaffabout
Terry F. Bidleman, Liisa M. Jantunen, Paul A. Helm, Eva Brorström‐Lundén, Sirkka Juntto

Bibliographic record

VenueEnvironmental Science & Technology · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChlordaneArcticEnantiomerEnvironmental chemistryChemistryTemperate climateEnvironmental sciencePesticideEcologyBiologyStereochemistry

Abstract

fetched live from OpenAlex

A 14-year data set (1984-1998) for chlordane compounds in arctic airwas examined to discern temporal trends. trans-Chlordane (TC), cis-chlordane (CC), and trans-nonachlor (TN) declined significantly (p < 0.001-0.02), with apparent times for 50% reduction of 4.9-9.7 y. The isomer fraction of TC = (TC/(TC + CC) also declined significantly (p < 0.001 -0.014) over the same time period. The enantiomeric composition of TC and CC was determined in air samples collected at arctic stations in Canada (1993-1996), Russia (1994), and Finland (1998), and a temperate station on the Swedish west coast (1998). Enantiomer fractions, EF = (+)/[(+) + (-)], were significantly different from measured EFs of racemic standards (0.498-0.501) at all stations for TC (p < 0.001) and two stations for CC (p < 0.001 to <0.05). These observations suggest changing source composition of chlordane in arctic air, with a greater proportion of weathered residues in recent years, possibly derived from soils. Identification of nonracemic (mean EFs = 0.662-0.703) heptachlor exo-epoxide (HEPX) at the four air stations further exemplifies contributions of soil emissions to long-range transport of chlordane-related compounds.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
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
Bibliometrics0.0000.002
Science and technology studies0.0000.008
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.188
Teacher spread0.183 · 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 teacher head, not a consensus.

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

Citations204
Published2002
Admission routes2
Has abstractyes

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