Bibliographic record
Abstract
Canadians are concerned about the abundance and quality of our freshwater resources, yet information is widely dispersed and often difficult to obtain. This publication reviews a wide array of data and government publications to assess the state of Canada’s water resources in an effort to make the information more accessible to policy-makers and the general public.According to World Bank data, Canada has the fourth largest supply of annual renewable freshwater in the world. However, most major sources are situated far north of our population centres, southern supplies have been declining in recent decades, and we have relatively high per-capita usage (9th) compared to other countries. Nonetheless, Canadians still consume only a small fraction (1.6%) of what is annually available. Furthermore, there are proven and effective policy tools like water pricing and allocation markets that can improve management in areas that currently experience seasonal shortages.Canada ranks 9th in the world for water quality on the basis of a small subset of water-quality parameters (dissolved oxygen, pH, conductivity, phosphorus, and nitrogen). Furthermore, calculations based on data over time from specific water monitoring stations with a larger parameter set indicate that water quality across Canada appears to have been stable at most stations since the 1990s. In addition, nutrient levels in major Canadian rivers and lakes have largely remained stable between 1990 and 2006. To obtain a more comprehensive picture of how Canadian water quality has changed over time, Canadian Environmental Indicators — Water reviews numerous government reports from each province. When we examine evidence from individual provinces over the long term, it is clear that, for many forms of pollution, water quality has improved greatly since the 1970s. In Ontario, total phosphorus has generally decreased in lakes and rivers since the 1970s. There has also been a general decline in mercury, PCBs, and many other toxic substances in the waters of Ontario and Quebec. Another example of improving water quality is the return to pre-settlement levels of total phosphorus in Lake Osoyoos in British Columbia. Bacteria levels are decreasing in major Alberta rivers from improvements to sewage treatment. Due to improvements in the bleaching process used in British Columbia’s pulp and paper mills, the province’s rivers have seen a significant decreases in chloride levels since the 1980s. Evidence from Ontario suggests that pesticides and pharmaceuticals in drinking water and chloride in rivers from road salt are currently not at a level to prompt concern for water quality.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.027 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".