Ontario Geological Survey response to the 2015 groundwater geoscience knowledge GAP analysis
Bibliographic record
Abstract
In March 2015, the Ontario Geological Survey (OGS) and Geological Survey of Canada (GSC) hosted a Groundwater Geoscience Knowledge GAP Analysis session for southern Ontario clients. The session objectives were to solicit input at the planning phase of several large OGS/GSC collaborative mapping initiatives, and to discuss the future of provincial government data management and the potential for accessing data via an "open data" initiative. Session participants identified 30 individual groundwater geoscience knowledge gaps, which fall into 7 categories comprising: i) communications, ii) standards and protocols, iii) hydro and geochemistry, iv) surface and groundwater interaction, v) geology and hydrogeology, vi) climate change and vii) data management and dissemination. In the past year, the OGS has taken significant steps to address many of the knowledge gaps that were brought forward at the March 2015 session. Communication issues represented the first, and most prominent, category of identified gaps. Session participants agreed that better communication between government ministries and agencies, that hold various land resource and science based mandates, would break down barriers between disciplines and create opportunities for multi-disciplinary collaboration. To address communication issues, the OGS has taken several positive steps to engage with partner land-based ministries. Some highlights of the activities emerging from these new connections include; the OGS providing geoscience mapping products and offering expertise to MOECC Land and Water Policy Branch as they evaluate land-use planning in the Greater Golden Horseshoe region; the development of a new OGS project, in collaboration with MOECC, to map shallow karst using geochemical indicators of rapid recharge; opening communication and information sharing to discuss the inclusion of OGS continuously cored boreholes with monitors into the MOECC Provincial Groundwater Monitoring Network; the creation of a working group to write a White Paper supporting a modern provincial government data strategy; and providing groundwater hydrochemistry mapping and expertise to support policy development for homeowner and public health unit notification when domestic well sampling results exceed drinking water guidelines from natural/geological sources. Each of the new projects and collaborations represents an improvement to inter-government communication. This list also demonstrates the OGS's commitment to create geoscience mapping products that meet the needs of clients, including those making science based policy decisions regarding groundwater. The OGS will continue to engage with clients and stakeholders as we continue our groundwater mapping initiative in southern Ontario in collaboration with the Geological Survey of Canada.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".