Canadian A.I. Removal Rate Estimator (CAIRRE): an artificial intelligence model to predict the removal of chemicals in secondary wastewater treatment plants
Bibliographic record
Abstract
Understanding the removal of a chemical in a wastewater treatment plant (WWTP) is important when performing chemical risk assessments. Chemicals undergoing assessment often have limited experimental measurements of physicochemical properties, biodegradation rates, and WWTP removal efficiencies. Models available to risk assessors to predict WWTP removal efficiencies are best used with high-quality input data and knowledge of plant conditions, information often unavailable when performing chemical risk assessments. In this work, we outline the development of the Canadian A.I. Removal Rate Estimator (CAIRRE), an artificial intelligence (A.I.) model suite designed to estimate removal efficiencies from secondary WWTPs. CAIRRE was trained on median experimental removal efficiencies for 161 chemicals across 59 secondary WWTPs in Canada, the USA (California), and various other locations curated from literature. The CAIRRE regression model has a validation Pearson R2 of 0.81 based on leave-one-out-validation (LOOV) results. When used to predict effluent concentrations for a test set containing 53 chemicals not seen during model training, CAIRRE was able to reproduce experimental observations with a Pearson R2 of 0.91. The CAIRRE model outperformed existing mechanistic and fugacity WWTP models which rely on physical-chemistry and biodegradation data provided by the user. This work demonstrates that the A.I. modeling approach taken in the development of CAIRRE is a promising strategy for predicting removal efficiencies of chemicals from secondary WWTPs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".