Agricultural Systems Planning:Farmland Conservation policies in Oregon and Ontario
Bibliographic record
Abstract
High quality agricultural land is a valuable non-renewable natural resource. Ontario is blessed with some of the best farmland in Canada, but as much of it is located in areas of intense urban growth pressure, it is declining and under continuous threat. Historically, planning for agriculture in Ontario has primarily consisted of identifying and protecting the soils and lands best suited to farming. There is, however, an emerging consensus that this land-based approach alone is not effective in supporting a thriving agricultural sector, particularly in the face of strong urban growth pressures. The Ontario government is now moving towards planning for "agricultural systems" - a more holistic concept which includes the land base, farm operations, and associated business, services, and infrastructure. This policy shift is most evident in its ongoing coordinated review of the four-land use plans relevant to the Greater Golden Horseshoe (GGH)” a region of both unparalleled agricultural lands and tremendous population and growth pressure. While no other jurisdiction has done exactly what Ontario is proposing, others have a longer history of more protective agricultural land policies. Oregon is one such jurisdiction, having adopted very protective policies in the 1970s. This project explores Oregon’s policy and its outcomes for farmland for lessons learned which may be relevant in the GGH as Ontario shifts to an “agricultural systems” approach. Oregon’s Willamette Valley is used as a case study region, its high-quality soils and strong growth pressure making a compelling parallel with the GGH.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".