Fourth Interim Report of the Standing Senate Committee on Energy, the Environment and Natural Resources
Bibliographic record
Abstract
It is an incontrovertible fact that we cannot live without water. Like air, water is a basic need. Water is sometimes described as "the provider of the infrastructure for life." It is fundamentally important. As Canadians, we generally don't spend much time thinking about water because we assume that there is plenty of it in this country to which we have ready access. Because most of us don't pay very much for water, we tend to take it for granted. We don't think we have a problem. The fact is that certain regions of Canada, notably in the prairies, face important water challenges. Some parts of the prairies are semi-arid. In certain areas water consumption now matches or possibly exceeds what is renewed every year. Your Committee heard from reputable scientists who consider that Alberta is the area of greatest concern because "in addition to being an extremely arid part of the country, it is developing rapidly." Demand for water typically rises in tandem with population growth and economic expansion. Rapidly growing cities and municipalities, as well as ranchers, farmers and industrial users, such as oil and gas producers, all compete for access to water. Scarce prairie water is used to grow feed for cattle, flush toilets, and, increasingly, to extract oil and gas. It is also used to extract and upgrade bitumen.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.042 | 0.026 |
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".