Increasing cannabis use and importance as an environmental contaminant mixture and associated risks to exposed biota: A review
Bibliographic record
Abstract
For years, cannabis has been largely used for its mind-altering properties. However, the discovery of its medicinal and therapeutic attributes has led to increased medicinal use. With recent legalization of cannabis in Canada, there is an evolving proliferation of the commercial availability of cannabis-containing products, and changes to patterns of use amongst adults are anticipated. Research into the potential harmful effects from increased use due to legalization have begun in humans; however, to our knowledge, investigations into the environmental occurrence and outcomes in fish and wildlife have been largely overlooked. Increasingly potent cannabis strains are also entering both commercial and medicinal sectors thus adding to the potential risk that this product poses to the environment. Indeed, emerging evidence reveals that current wastewater treatment has limited ability to remove bioactive components of cannabis thus allowing their entry into aquatic ecosystems Furthermore, there is very little known regarding the effects, mechanisms and impacts of cannabis exposure in exposed biota, and is currently limited to a few lab-based and field-based studies in a handful of fish species (e.g. zebrafish). This review will discuss the therapeutic uses of cannabis and its constituents, as well as examine its environmental fate and potential to affect aquatic ecosystems in Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".