Machine learning in wastewater: opportunities and challenges — “not everything is a nail!”
Bibliographic record
Abstract
This paper highlights the potential of machine learning (ML) for wastewater applications, with a focus on key applications and considerations. It underscores the need for simplicity in ML models to ensure their interpretability and trustworthiness, cautioning against the use of overly complex ‘black box’ models unless absolutely necessary, especially with limited data. Not all modelling problems should be considered nails for which the ML hammer is the best-available tool. We emphasise the critical role of thorough data collection, including metadata, given its scarcity in some areas. Future research is encouraged to develop benchmark hybrid models to bridge the educational gap for environmental engineers and to establish best practices for managing data and model metadata, thereby improving ML’s accessibility and utility in wastewater applications. • Machine learning has arrived in a wide diversity of wastewater applications. • Simplicity must be pursued to ensure ML-interpretability and trustworthiness. • Successful ML needs high quality data, meta-data and bridging the educational gap. • Hybrid (ML-mechanistic) models tap into both data scientist and engineer expertise.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
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".