Assessing the potential of machine learning methods to study the removal of pharmaceuticals from wastewater using biochar or activated carbon
Bibliographic record
Abstract
Pharmaceuticals in wastewater are rapidly becoming new emerging pollutants, affecting humans and the aquatic ecosystem, and can go undetected due to their microscopic nature. Adsorption proves to be a promising technology for the removal of pharmaceuticals from effluent wastewater owning to its low cost, flexibility, and renewability. Adsorbents are porous materials such as silica, clay, resins, and carbon-based materials (e.g., biochars, carbon nanotubes, and activated carbon) often used to remove pharmaceutical micropollutants during adsorption. Among them biochar is an emerging, cost-effective, and eco-friendly sorbent. Modeling methods such as linear correlativity and multilinear regressions, are often employed to explain the adsorption mechanism, however they show limited accuracy and applicability. On the contrary, data driven machine learning (ML) methods is a powerful tool that could be used to study the complex relationship between adsorption performances and biochar properties. This review provides an overview of recent advances in the use of machine learning (ML) methods to explore the field of pharmaceutical adsorption onto biochar. An introduction to different ML algorithms and their advantages and limitations is provided. Furthermore, the challenges and future prospects of ML applications to study the adsorption mechanism is outlined.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".