Bibliographic record
Abstract
Even in water-rich Canada, many jurisdictions are having trouble providing adequate, clean fresh water as their populations not only grow but exhibit higher expectations for water availability and water safety. The conventional approach to such problems accepted the history of constantly growing demand for water and responded by extending pipelines, constructing more dams and drilling deeper. The alternative to this engineering approach is to put greater emphasis in demand-side policies promoting water efficiency and conservation. Full-cost pricing along with better information and education programs can help a great deal, but will not likely be sufficient to meet future water problems. Fortunately, there is a stronger, albeit normative, demand-side alternative called the water soft path, which is modelled on the highly successful approach known as the soft energy path. Soft paths can be described as approaches to natural resources management that rely on a multitude of relatively small-scale and renewable sources of supply coupled with ultra-efficient ways of meeting end-use demands. This paper will contrast water soft paths with the conventional (hard path) approaches, and then review the methodology and feasibility of soft path analysis.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".