A framework for screening sites at risk from contaminants of emerging concern
Bibliographic record
Abstract
Trace levels of a variety of currently unregulated organic chemicals have been detected in treated wastewater effluents and surface waters that receive treated effluents. Many of these chemicals of emerging concern (CECs) originate from pharmaceuticals and personal care products that are used widely and that frequently are transported "down the drain" to a wastewater treatment plant (WWTP). Actual effects of CECs on aquatic life have been difficult to document, although biological effects consistent with effects of some CECs have been noted. There is a critical need to find appropriate ways to screen wastewater sites that have the greatest potential of CEC risk to biota. Building on the work of several researchers, the authors present a screening framework, as well as examples based on the framework, designed to identify high-risk versus lower-risk sites that are influenced by WWTP effluent. It is hoped that this framework can help researchers, utilities, and the larger water resource community focus efforts toward improving CEC risk determinations and management of these risks.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".