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Record W4205723500 · doi:10.1002/rem.21706

PFAS Experts Symposium 2: PFAS Toxicology and Risk Assessment in 2021—Contemporary issues in human and ecological risk assessment of PFAS

2022· article· en· W4205723500 on OpenAlexaff
Jeanmarie Zodrow, U. Vedagiri, Tamara L. Sorell, L.P. McIntosh, Emily Larson, Linda C. Hall, Michael L. Dourson, Linda Dell, Douglas Cox, Krista Barfoot, Janet K. Anderson

Bibliographic record

VenueRemediation Journal · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsRisk assessmentHuman healthEnvironmental healthEnvironmental planningRisk analysis (engineering)Environmental scienceMedicineComputer science

Abstract

fetched live from OpenAlex

Abstract This paper summarizes key information on topics of contemporary interest in human and ecological per‐ and polyfluoroalkyl substance (PFAS) risk assessment, which were discussed at the PFAS Experts Symposium 2. For human health, the discussion focused on the toxicologic and epidemiologic endpoints and exposure assumptions that contribute to differences in PFAS regulatory criteria. For ecological risk, the discussion assessed the current state of the science available to support ecological screening levels and identified key data gaps and uncertainties in our understanding of ecological exposure and toxicity. Finally, the paper summarizes a panel discussion that addressed the challenges and uncertainties of regulating PFAS as a class.

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)0.021
metaresearch head score (Gemma)0.011
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0070.004
Open science0.0020.004
Research integrity0.0200.010
Insufficient payload (model declined to judge)0.0080.003

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 head0.022
GPT teacher head0.341
Teacher spread0.318 · 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

Citations20
Published2022
Admission routes1
Has abstractyes

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