Farmland Preservation: Innovative Approaches in Ontario
Bibliographic record
Abstract
Policy to protect farmland is based on the premise that it is in the public interest to protect land, farmers, and the farm economy. Within Ontario, high capability farmland is a finite resource. Ontario contains 52 percent of Canada's Class 1 land even though only 6.8 percent of the province's total land area is suitable for agriculture. The combination of quality soils with the excellent climate of Southern Ontario makes this the prime area for agricultural production in all of Canada. With an abundance of heat units and rainfall, Ontario is able to produce crops that are not viable elsewhere in the country. (Large acreages of farmland on the prairies have excellent soil, but a much more limiting climate.) The result is a diverse and active agricultural industry that led all Canadian provinces in 2001 in gross farm receipts. While the importance of Ontario's agricultural industry is generally recognized, the commitment of the public and different levels of government to its long-term protection has at times wavered. Recent developments, however, show a renewed interest on the part of the provincial government to aggressively address the issue of farmland loss. In addition, numerous groups—farmers …
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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 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".