From atmosphere to basement: development of a framework for groundwater assessment in Canada
Bibliographic record
Abstract
The Geological Survey of Canada (GSC) is helping map and assess the availability of groundwater resources in Canada. This effort is set within complex jurisdictions of surface water and groundwater resources in Canada where there is often no clear division between the federal and provincial governments. Responsibilities are often shared, with the federal government sharing responsibility on water issues for federal lands, territories (e.g. Nunavut), First Nation lands, boundary and transboundary waters (e.g. Great Lakes, Spiritwood aquifer), navigable waterways and where fisheries resources are concerned. Consequently, much government work completed on groundwater in Canada is done by the provinces with collaborative support from the GSC, the oldest government research institution in the country. To advance groundwater assessment in Canada, the National Ad Hoc Committee on Groundwater proposed a framework for a national co-operative program (Rivera et al., 2003). At the same time, the groundwater program of the GSC developed a strategy to map and assess 30 key aquifers across the country (Figure 1), along with a plan to remove accessibility barriers to data discovery and retrieval (Boisvert and Broderic, 2011). The GSC is also developing a synoptic understanding of the groundwater resources in Canada by using the hydrogeological regions (Figure 1, Table 1). This paper provides an overview of the conceptual framework and methods employed to achieve these objectives. The approach is founded on a traditional basin analysis methodology (i.e., geology) with an objective of understanding the geological history of the basin to inform future work and provide a predictive framework in areas of sparse, inadequate data and hence basin knowledge. This approach is being extended from the traditional subsurface basin context to encompass the hydrological cycle and hence understanding from atmosphere to basement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".