The Detection, Monitoring, and Removal of Pharmaceuticals From Wastewater: Does the Current Canadian Chemical Management Framework Protect Ecological Health in Receiving Water Sources?
Bibliographic record
Abstract
The presence and ecological impacts of pharmaceuticals on the environment continue to be a subject of environmental and public health concern for developed and developing countries. While there are no detailed indications to support their impacts on human health, research has shown that they incite negative ecological responses in aquatic organisms. Despite the evidence, there has been no regulation to remove or limit the rate at which they are released into water sources. The big question, which has surrounded the subject with a reasonable level of scientific uncertainties, remains: is there still a reason to be worried about their presence in the environment from an ecological point of view? This thesis examined the position of science regarding the gaps and uncertainties that continue to exist concerning the presence of pharmaceuticals in the environment. It also evaluated the management strategies that are currently in place in Canada to protect ecological health, comparing Canada with other international jurisdictions (the United States and European Union) in terms of regulations and management frameworks to see if there are lessons to be learned from the response of international jurisdictions to the presence of pharmaceutical compounds in wastewater.
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".