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Worldwide Regulatory Guidance Values for Chlorinated Benzene Surface Soil Contamination

2012· article· en· W2016067594 on OpenAlexaboutno aff
Emily S. Kowalsky, Aaron A. Jennings

Bibliographic record

VenueJournal of Environmental Engineering · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsnot available
FundersU.S. Environmental Protection Agency
KeywordsHexachlorobenzeneContaminationEnvironmental scienceChlorobenzeneLog-normal distributionEnvironmental chemistryMaximum Contaminant LevelEnvironmental engineeringMathematicsStatisticsPollutantChemistryEcologyBiology

Abstract

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Surface soil contamination is a worldwide problem that is often addressed by the use of regulatory guidance values (RGVs) that establish the maximum amount of contamination that may exist without prompting a regulatory response. Chlorinated benzenes are a family of 12 synthetic aromatic contaminants for which at least 168 jurisdictions have promulgated guidance values. Analysis of the 973 RGVs for the eight most commonly regulated chlorinated benzenes is presented. The RGVs of chlorobenzene, 1,2-, 1,3- and 1,4-diclorobenzene, 1,2,4-trichlorobenzene, 1,2,4,5-tetrachlorobenzene, and hexachlorobenzene vary by over six orders of magnitude. The RGVs for pentachlorobenzene vary by five orders of magnitude. The RGV distributions resemble those of lognormal random variables, but contain nonrandom clusters and extreme values that deviate from the lognormal distribution model. Point clusters appear to reflect the influence of organizations such as the U.S. Environmental Protection Agency (USEPA) and the Canadian Council of Environmental Ministers. Uncertainty analysis applied to the USEPA RGV derivation model indicates that only about 39% of all RGVs fall within easily justified uncertainty ranges. Methods are discussed to help reduce the remaining RGV variability.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.669
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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.

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

Citations11
Published2012
Admission routes1
Has abstractyes

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