Protecting the Future of the Agri-food Sector Exploring Innovative Tools for Farmland Preservation and Agri-Environmental Stewardship
Bibliographic record
Abstract
A vibrant agri-food sector is dependent on a reliable land base, but population growth and associated urban development have put pressure on the availability of farmland in southern Ontario, where most of Ontario’s prime agricultural lands are located. While it is apparent that prime agricultural land is being converted to non-farm land uses, data quantifying this conversion is inconsistent, making it challenging to understand the full extent of the issue. To address this challenge, a methodology was developed to track farmland conversion. The most accurate data available for assessing the conversion of prime agricultural lands to non-farm uses are official plan amendments, which must be approved before any development can begin. This session will provide an overview of an ongoing research project to clarify and characterize the conversion of prime agricultural land in southern Ontario. Previous research has tracked changes in the conversion of prime agricultural land in southern Ontario from 2000 to 2016. This research extends that period and uses the same methodology to cover the latest census period, from 2017 to 2022. By updating the data and tracking changes in the conversion of prime agricultural lands over a longer period, this research will provide a more comprehensive picture of the issue. This data can help inform policies and strategies that support the preservation of prime agricultural lands and the growth of the agri-food sector in southern Ontario.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".