Comprehensive groundwater data management and analyses - raising the bar
Bibliographic record
Abstract
"What matters gets measured" "You can't manage what you don't measure". When it comes to Ontario's groundwater resources these old adages certainly strike a chord. What do we really know about our groundwater systems? Have we been sufficiently measuring and monitoring the resource? Do we effectively integrate some 50 years of previous knowledge into day to day decision making? Since 2001, the long standing Oak Ridges Moraine Groundwater Program (ORMGP - formerly referred to as YPDT-CAMC Groundwater Management Program) has been working to: i) assemble a comprehensive and reliable source of groundwater related data; ii) bring critical analyses to the data; and iii) construct a geological and hydrogeological framework into which new drilling and information can be incorporated. The results of this work are being made available through an interactive website where numerous 'themed' maps (e.g. geology, water levels, documents, etc.) intuitively provide ready access to the program's data and interpretive geological and hydrogeological framework. As an integral part of the program's over-arching goal of improving upon water management decision-making in Ontario, an ongoing process is to engage practitioners on a variety of levels to arrive at a point where regular contributions of data, insight and information are returned back to this 'actively managed' program, thus improving the hydrogeological knowledge-base for the broader community.
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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 | 0.050 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".