Bibliographic record
Abstract
Farmland in Ontario, Canada is under immense pressure from development associated with population growth and urbanization.The future sustainability of agriculture in Ontario is dependent upon a stable land base and precise understanding of the availability of farmland; however, in many communities, farmland is sacrificed for residential subdivisions, commercial developments and aggregate operations, among others.The provincial government has recognized the development threats facing farmland and created the Greenbelt Act (2005) to protect prime farmland and other sensitive landscapes.While this protectionist policy has appeared to stop some development, farmland continues to be lost to non-farm land uses and a policy failure is assumed.In reality, much of this land was designated decades prior for urban development but the loss is not evident until urban development begins.In order to assess the strength of the Greenbelt policy and understand the amount of farmland lost, quantitative data at a region or county level is needed.Currently, no accurate data regarding the amount of farmland lost to other land uses exists.This presentation will explore a new methodology for measuring the loss of farmland through official plan amendments on private property in southern Ontario.Analysis of data in the form of a case study is presented from 2 counties and regions.This highlights the amount of farmland converted to other land uses both before and after Greenbelt Act (2005) came into force.The economic and environmental impacts of farmland loss and the role of planning policies will also be discussed.This analysis and methodology will be applicable in many different jurisdictions.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".