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PCBs, Dioxins, and Related Compounds

2003· book-chapter· en· W4388331450 on OpenAlexaff
Donald T. Wigle

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsInstitute of Population and Public HealthUniversity of Ottawa
Fundersnot available
KeywordsEnvironmental chemistryBioaccumulationFood chainChemistryBiodegradationToxicologyEnvironmental scienceWaste managementOrganic chemistryEcologyBiologyEngineering

Abstract

fetched live from OpenAlex

Abstract Polyhalogenated aromatic hydrocarbons (PHAHs) comprise a large group of semi-volatile chemicals that are stable at high temperatures, highly soluble in lipids, and resistant to biodegradation. Unfortunately, these properties enable PHAHs to disperse and persist in the environment, to bioaccumulate in terrestrial and aquatic food chains, and to cause unforeseen adverse health effects among wildlife and humans. All of us probably have detectable PHAHs in our bodies, the concentrations of PCBs generally being much higher than those of other PHAHs. Chlorinated, brominated, and mixed halogenated PHAHs have similar structures and toxicity but widely variable potencies; major subgroups include biphenyls, dibenzo-p-dioxins, and dibenzofurans (Table 6–1). Monsanto, the sole manufacturer of PCBs in the United States, produced about 700,000 tons during the period 1929–1979, annual output peaking in 1970 at 43,000 tons. Given their high heat capacity and stability, PCBs were ideal for uses in heat-resistant solvents, sealants, and lubricants, and as dielectric fluids in electrical transformers, fluorescent light ballasts, and other electrical equipment. The most intensely studied PHAH is 2,3,7,8tetrachloro-p-dibenzodioxin (TCDD), one of the most potent known toxicants (Schiestl et al., 1997).

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.045
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0450.033

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 head0.009
GPT teacher head0.198
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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2003
Admission routes1
Has abstractyes

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