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Record W2766953264 · doi:10.1002/etc.4013

Use of prospective and retrospective risk assessment methods that simplify chemical mixtures associated with treated domestic wastewater discharges

2017· article· en· W2766953264 on OpenAlexaff
Jerome M. Diamond, Rolf Altenburger, Anja Coors, Scott D. Dyer, Michael J. Focazio, Karen A. Kidd, Albert A. Koelmans, Kmy Leung, Mark R. Servos, Jason Snape, Johannes Tolls, Xiaowei Zhang

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of WaterlooMcMaster University
Fundersnot available
KeywordsEffluentActivated sludgeEnvironmental scienceRisk assessmentTrickling filterWastewaterHazard quotientSewage treatmentTrophic levelEnvironmental chemistryChemistryEnvironmental engineeringEcologyBiologyComputer science

Abstract

fetched live from OpenAlex

A framework is presented that is intended to facilitate the evaluation of potential aquatic ecological risks resulting from discharges of down-the-drain chemicals. A scenario is presented using representatives of many of the types of chemicals that are treated domestically. Predicted environmental chemical concentrations are based on reported loading rates and routine removal rates for 3 types of treatment: trickling filter, activated sludge secondary treatment, and activated sludge plus advanced oxidation process as well as instream effluent dilution. In tier I, predicted effluent concentrations were compared with the lowest predicted-no-effect concentration (PNEC) obtained from the literature using safety factors as needed. A cumulative risk characterization ratio (cumRCR) < 1.0 indicates that risk is unlikely and no further action is needed. Otherwise, a tier 2 assessment is used, in which PNECs are based on trophic level. If tier 2 indicates a possible risk, then a retrospective assessment is recommended. In tier 1, the cumRCR was > 1.0 for all 3 treatment types in our scenario, even though no chemical exceeded a hazard quotient of 1.0 in activated sludge or advanced oxidation process. In tier 2, activated sludge yielded a lower cumRCR than trickling filter because of higher removal rates, and the cumRCR in the advanced oxidation process was << 1.0. Based on the maximum cumulative risk ratio (MCR), more than one-third of the predicted risk was accounted for by one chemical, and at least 90% was accounted for by 3 chemicals, indicating that few chemicals influenced the mixture risk in our scenario. We show how a retrospective assessment can test whether certain chemicals hypothesized as potential drivers in the prospective assessment could have, or are having, deleterious effects on aquatic life. Environ Toxicol Chem 2018;37:690-702. © 2017 The Authors. Environmental Toxicology and Chemistry Published by Wiley Periodicals, Inc. on behalf of SETAC.

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)0.014
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.313
Teacher spread0.292 · 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 designSimulation or modeling
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

Citations42
Published2017
Admission routes1
Has abstractyes

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