Groundwater knowledge management for southern Ontario: An example from the Oak Ridges Moraine
Bibliographic record
Abstract
The interest surrounding groundwater protection in Southern Ontario has grown considerably since the Walkerton tragedy of May 2000. Since that time, a number of technical studies have been undertaken by the Province to support the preparation of Source Water Protection plans. Underlying all sound water resource management initiatives in Ontario is the need for a renewed focus on the fundamentals, specifically in this case a focus on groundwater knowledge and its management. Using the Oak Ridges Moraine Hydrogeology Program as a unique example of a groundwater “knowledge management” system, this paper presents some unexplored opportunities that merit further consideration in the application of a “knowledge management” philosophy within Ontario’s overall water management framework. Within the Program’s study area, the linkage and integration of the Water Well Information System with other borehole datasets and consulting reports, as well as with water use (e.g. municipal pumping) and water quality databases, has created perhaps the most comprehensive, actively managed groundwater “knowledge management” system in Canada. Ongoing Source Water Protection and other work undertaken through consultants, including data and geological/hydrogeological interpretations, is being re-incorporated, where sound and appropriate, into the program’s existing groundwater knowledge infrastructure. The program’s groundwater “knowledge management” system has been developed with a long-term (i.e. multi-decade) water management time frame in mind and is made accessible to geoscientists undertaking work in the area.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".