Are we on the right track? A panel discussion on the future direction of groundwater management in Ontario
Bibliographic record
Abstract
This panel discussion will focus on whether, as a Province, Ontario is adequately planning for the future of our water resources. Recent developments related to the Greater Golden Horseshoe Growth Plan, the Greenbelt Plan, the Niagara Escarpment Plan and the Oak Ridges Moraine Conservation Plan, as well as the expected impacts of climate change, will all figure into the conversation. Although the dialogue among panelists is expected to look broadly at water management and the current state of efforts to prepare for the future, there will be an emphasis on groundwater resources. Decisions affecting water resources are made by policy makers, technical staff, and those responsible for issuing approvals on a daily basis. These decisions range from land use planning to water allocation targets to wastewater treatment requirements. It has been almost 20 years since the tragic events of Walkerton highlighted additional needs for improved management of Ontario's water resources and in particular its groundwater. With the passing of the Clean Water Act in 2006, the past decade has seen considerable investment, primarily through the Drinking Water Source Protection program. This investment has ranged from the collection of water related data, the technical synthesis of these data, in many cases into sophisticated numerical groundwater models, and an overall improvement in our understanding of how water moves through Ontario's watersheds. In the current transition to a new government with a focus on curtailing public expenditures, what is the most appropriate way forward to capitalize on this investment and to ensure that future practitioners do not lose the knowledge gained from these activities? Is it even in danger of being lost? Panel members bring a diverse range of views and expertise to the discussion and will be prepared to offer their insights into some of Ontario's key ongoing and upcoming water related challenges and opportunities. Are Ontario's water related policies and programs being effectively implemented to support long-term sustainability of the resource? How are we monitoring the impact of our decisions? Are our actions pro-active or reactive? Is the acquisition of water related data sufficient to inform decision-making? Are the data adequately managed and readily accessible to those making planning and technical decisions? Is research headed in the right direction? Are agencies at the Federal, Provincial and Municipal levels sufficiently engaged and coordinated? Being blessed with a seemingly abundance of water, has Ontario been too complacent in how we currently make decisions with respect to our water resources? Following introductory comments from panel members, there will be an engaging and insightful discussion surrounding the future of water management in Ontario.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".