Groundwater permits in Ontario: an analysis of open data
Bibliographic record
Abstract
Groundwater is vital to sustainability in Ontario and Canada. It is a source of water for agriculture, industrial, commercial organizations, hundreds of communities and millions of residents. Despite its significance, the management of groundwater faces substantial challenges, including knowledge gaps, contamination risks, and climate change impacts. This study examines groundwater use in Ontario through the analysis of two open datasets: the Ontario Permit to Take Water (PTTW) dataset, covering the period from 1960 to 2022, and the Reported Water Use dataset, which includes data on the most recent water usage reports from 2020. Using statistical and geospatial analyses, this research identifies patterns in water permit allocation and utilization, providing an analysis of permit data and the critical role of groundwater in Ontario’s economy and water supply. Findings reveal significant groundwater use and important regional and sectoral variations highlighting the necessity for integrated policy and management practices that consider groundwater more centrally in the allocation of Ontario’s water. The study also underscores the importance of open, transparent water data as the foundation for sustainable management and stakeholder engagement to enhance water governance. Finally, the paper provides some policy recommendations stemming from the data analysis related to water resource management and suggests directions for future research to ensure sustainable groundwater use aligns with broader environmental, economic and social objectives.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".