Bibliographic record
Abstract
Conservation Authorities are local level, water resource management organizations; jurisdictionally defined on the watershed-scale. Both local and regional land use decision making process should be informed by a robust understanding of the geological framework and hydrogeological regime. This requires considerable multi-disciplinary expertise, funding, and an on going involvement by local stakeholders. For our respective conservation authorities, and by extension our municipal partners, the Ontario Geological Survey (OGS) has been the catalyst for local efforts to address data gaps to inform better land use decisions. The use of preliminary data generated from the OGS 3D Geological Model projects in the Niagara Peninsula and Central and South Simcoe areas have addressed information and knowledge gaps including but not limited to: lack of meaningful regional geologic cross-sections; extent and definition of aquifers; buried bedrock channel morphologies; distribution, thickness and composition of aquitards; refinement of Highly Vulnerable Aquifers, and geochemical anomaly characterization. This has been completed through improved hydrogeologic characterization via additional golden spike monitoring locations and baseline monitoring (hydraulic and geochemical), geophysical delineation, and associated modelling. Early advantages of local utilization of the OGS project results have included the use of geological refinement in the development of an integrated MikeSHE model for drought management; information for rural development approvals and municipal groundwater exploration studies, collaboration on emerging chemicals of concern, and spatially improved groundwater monitoring. Importantly, the OGS results are also addressing items needed for source water protection planning but unavailable for source water protection funding: (i) research to address local data gaps and (ii) long-term water quality monitoring programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.029 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.021 |
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".