The science behind Ontario's water quantity management review
Bibliographic record
Abstract
Water is vital to the health and integrity of our ecosystems and communities. The anticipated impacts of climate change have intensified concerns related to water security in Ontario. Improving our water resource information and assessing our existing water quantity management tools is a key step in ensuring that Ontario will be able to protect and manage water resources now and in the future. The Ministry will provide an overview of work to modernize it's water quantity management framework (policy, program and science) to ensure a robust and adaptive approach to water resources management into the future, and the water quantity scientific work being undertaken to improve our understanding of water resources knowledge on Ontario. Key aspects of the water quantity science work being undertaken will be introduced, including: Immediate: development of a science & technical backgrounder; a review of science/jurisdictional best practices; assessment of Ontario's water (quantity) resources and management approaches in specific geographical areas; water quantity data enhancements and development of an internal data website / platform; water Quantity Protection External Working Group. Longer Term: outfacing water quantity website (data platform and tools); enhance source protection water budgets and models; new and enhanced science tools and approaches; province-wide and/or additional local scale water quantity assessment; enhanced monitoring. BluMetric will provide an overview of the water quantity assessment and management review work being undertaken in specific geographical areas in the province. Water Quantity Study Areas and Water Bottling Study areas being investigated as part of this work (Fig. 1).
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".