Advancing Canadian Wastewater Assets (ACWA) bridges laboratory-scale testing of wastewater technologies and effects on receiving environments
Bibliographic record
Abstract
Laboratory assessments of organism responses to wastewater are inexpensive, easily replicated, and offer control and precision, yet are often so reduced in temporal and spatial scale that results are difficult to apply to receiving environments. Whole-system experiments are expensive, lack true replication, and can be logistically challenging, yet offer the best insight as to how ecosystems will respond to effluent inputs. Advancing Canadian Wastewater Assets (ACWA), which includes a wastewater treatment plant, analytical labs, and research streams, provides unique infrastructure to test new wastewater treatment technologies, demonstrate technology benefits by direct analytical chemistry, and determine receiving environment effects. The ability to measure temperature, conservative ions, and dissolved oxygen in 12 replicated, naturalized streams allows physical modelling and biological monitoring consistent with larger, natural rivers. Assessments of receiving environment data could guide policy development for safe discharge of emerging contaminants and develop strategies to reduce development and persistence of antimicrobial resistance.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".