Before the Bulldozer Hits the Ground: Measuring Farmland Loss in Ontario
Bibliographic record
Abstract
Rural Ontario is in a constant state of change, as economic, environmental and political pressures impact the viability and resilience of many rural communities. Agricultural areas, in particular, are often negatively impacted by such changes, as this land may be more valuable for development purposes. Farmland is often redesignated to residential, commercial or aggregate land uses, among others, significantly impacting the viability of the agricultural industry. The future sustainability of agriculture in Ontario is dependent upon a stable land base and precise understanding of the availability of farmland. To date, accurate data regarding the amount of farmland being converted to non-farm land uses is not available as existing methods have significant limitations regarding data accuracy, consistency and timing. This research seeks to evaluate the current state of Ontario's farmland in terms of the land available and policies regarding land conservation. In order to ensure that farmland is available, it is necessary to measure the existing land base and determine the quantity of land being lost to development. This study has developed a new methodology for measuring the amount of farmland converted to non-farm land uses through official plan amendments and has been applied to nine regions and counties in southern Ontario. This presentation and poster will detail this new methodology and provide an analysis of the data collected to date. Recommendations regarding policy development, challenges associated with data collection and future research will also be presented.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".