Accumulation of antibiotics in the environment: Have appropriate measures been taken to protect Canadian human and ecological health?
Bibliographic record
Abstract
In Canada, every day, contaminants of emerging concern (CEC) are discharged from waste treatment facilities into freshwaters. CECs such as pharmaceutical active compounds (PhACs), personal care products (PCPs), per- and polyfluoroalkyl substances (PFAS), and microplastics are legally discharged from sewage treatment plants (STPs), water reclamation plants (WRPs), hospital wastewater treatment plants (HWWTPs), or other forms of wastewater treatment facilities (WWTFs). In 2006, the Government of Canada established the Chemicals Management Plan (CMP) to classify chemicals based on a risk-priority assessment, which ranked many CECs such as PhACs as being of low urgency, therefore permitting these substances to continue being released into the environment at unmonitored rates. The problem with ranking PhACs as a low priority is that CMP's risk management assessment overlooks the long-term environmental and synergistic effects of PhAC accumulation, such as the long-term risk of antibiotic CEC accumulation in the spread of antibiotic resistance genes. The goal of this review is to specifically investigate antibiotic CEC accumulation and associated environmental risks to human and environmental health, as well as to determine whether appropriate legislative strategies are in place within Canada's governance framework. In this research, secondary data on antibiotic CEC levels in Canadian and international wastewaters, their potential to promote antibiotic-resistant residues, associated environmental short- and long-term risks, and synergistic effects were all considered. Unlike similar past reviews, this review employed an interdisciplinary approach to propose new strategies from the perspectives of science, engineering, and law.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".