Numerical modelling - a key tool to support water management decisions in Ontario
Bibliographic record
Abstract
Since about 2000, Ontario's municipalities, conservation authorities, provincial ministries and consulting firms have been variously engaged in preparing technically sophisticated numerical models for the purposes of managing and protecting water supplies. Not only have these models led to an improved understanding of water quantity and water movement within the province's watersheds, but the work has also led to a comprehensive synthesis of water related information across much of the province. A resultant ongoing challenge for all parties is one of maintaining this new knowledge based infrastructure for future use. The challenge is a difficult one, in light both of the limited finances, as well as the limited technical modelling expertise available within the province. However to not make use and build upon the important work that has been undertaken, would be a disservice to Ontario's citizens. It is therefore incumbent on the community of practitioners to figure out a strategic path forward. The goal of this half-day session will be to initially shed light on the ability of numerical models to provide insights into flow system behaviour and thereby be a valuable tool for water resources management. The follow up panel discussion will touch upon various issues related to broadening the use of numerical models. One specific topic of interest will be model management, this being a very new endeavour, having only recently arrived at Ontario's doorstep in a significant way following on the extensive technical work undertaken through Source Water Protection. Through the construction and use of numerical models, consultants assemble a tremendous understanding of the how water moves in the subsurface and how it interacts with the surface water environment. The entirety of this understanding can never be fully conveyed in a summary report. Drawing upon their considerable expertise in the construction and use of numerical models, the speakers will highlight various instances where numerical models have been used, or could be used, to reveal flow system behaviours that can assist Ontario to improve water management related decisions. Following upon the talks, stick around after the break for an engaging and insightful panel discussion that will address a broad range of current issues surrounding the use of numerical models in water management decision-making. Can any numerical model be re-used/re-purposed for future decision-making given that it has been built for a specific purpose? Should models be considered out dated and obsolete once they have served their initial purpose? What are the limitations to such re-use and how should they be conveyed? Is it that only certain elements of a numerical model be used into the future? Who should ensure models are up-to-date and reflect the most current understanding prior to their re-use? How can high level technical and policy managers be made aware of the considerable insights that numerical models can bring to bear on water management decisions? As a community of technical practitioners, is there a need to train colleagues and staff at provincial ministries, municipalities, conservation authorities and consulting firms, to be more comfortable with the insights and analyses offered up through numerical modeling? How is this best achieved? Will this lead to increased use of numerical models as a key input to guide decision making? Is there perhaps a role for a structured peer review system whereby credible modelling experts are retained to assist in model re-use/re-purposing?
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".