Fresh Water-Related Indicators in Canada: An Inventory and Analysis
Bibliographic record
Abstract
The number of fresh water-related assessment indicators in Canada has proliferated rapidly over the past decade. This article presents a comprehensive review and evaluation of existing fresh water-related indicators in Canada, and analyzes the extent to which these indicators can be (and are being) used to guide effective water assessment. Specifically, the article presents an inventory of over 300 fresh water-related indicators, the first of its kind in Canada. This inventory is analyzed with respect to jurisdictional scale (federal and provincial), method, and topic/issue of focus. The results drawn from a national-level survey and follow-up interviews regarding the effectiveness and utility of assessment indicators are presented. The key drivers and trends in indicator development are then explored. These findings suggest that certain types of indicators and topics are under-represented, that important gaps and overlaps exist, and that indicators are not sufficiently adapted to the needs of decision-makers. This has resulted in systemic barriers to the effective use of indicators, which has reduced water assessment capacity 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.064 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".