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Record W2947685603 · doi:10.3808/jeil.201900001

Removal of Emerging Contaminants: The Next Water Revolution

2019· article· en· W2947685603 on OpenAlexafffund
Edward A. McBean

Bibliographic record

VenueJournal of Environmental Informatics Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsUniversity of Guelph
FundersHealth CanadaUniversity of GuelphNational Geographic SocietyU.S. Environmental Protection Agency
KeywordsKey (lock)Emerging technologiesBiochemical engineeringRisk analysis (engineering)Environmental scienceComputer scienceBusinessEngineeringComputer security

Abstract

fetched live from OpenAlex

Thousands of new, emerging chemicals are produced each year, making thorough investigations infeasible regarding their potential detrimental dimensions. As an important step for estimating whether a chemical will result in an exposure pathway and therefore create the potential for a detrimental impact, a coefficient-based strategy consisting of eight key coefficients, is proposed. The strategy is based upon key factors which are used to assess the potential for a chemical to attenuate or change its phase or medium, as part of its fate and transport pathway. The eight key coefficients are described, knowledge of which will assist in determining whether a chemical will result in a fate and exposure pathway change and/or attenuate, as a means of developing a strategy to assess the risks of emerging contaminants. The need for attention to this next water revolution to develop a strategy to assess some of the risks of emerging contaminants is already upon us.

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)0.003
metaresearch head score (Gemma)0.005
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: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.008
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.004
GPT teacher head0.169
Teacher spread0.165 · 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
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

Citations15
Published2019
Admission routes2
Has abstractyes

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