Are we about to reliably predict the fate of micropollutants through wastewater treatment plants?
Bibliographic record
Abstract
Models for micropollutants (MPs) are required to provide engineers and decision-makers with reliable tools that will help increase MP removal through wastewater treatment plants (WWTPs) and minimize their impact on the receiving waters. This paper aims at identifying the modelling needs of research (fundamental) vs. practice (design) vs. regulation (compliance) with a focus on the interactions. Different model developments that have occurred recently are first presented with a discussion on the key-question of the 'optimum complexity'. Sampling strategies are also discussed since they influence the model uncertainty. Finally, the paper proposes suggestions for how models might be modified to incorporate our evolving knowledge about MP fate to improve model utility into the future. For instance, the relative role of heterotrophic bacteria versus ammonia oxidizing autotrophic bacteria towards the fate of MPs should be addressed, and the elaboration of a unified agreement on experimental protocols would be necessary to advance model calibration. The degree to which by-products that have ecotoxicological relevance are created during treatment is also a key issue that needs to be considered in future model as well as the use of ecological models as a means to benchmark modifications to wastewater treatment.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".