Meeting the Challenges of Sustainable Soil and Water Resource Use for Food Production in Ontario, Canada
Bibliographic record
Abstract
Expanding urban-industrialization in southern Ontario is vying for use of Canada's best farmland. Agriculture's response has been to re-structure, to mechanize and automate, to increase usage of imported synthetic inputs, all at the expense of environmental protection, natural resource stewardship and sustainability. Widespread adoption of organic farming systems would do much to mitigate the stewardship and sustainability problems, but too many impediments exist to prevent this. Adoption of reduced-input farming techniques would offer a partial solution to sustainability problems. Additional measures in the form of public intervention should be employed. Public policies aimed at inducing farmers to expend more conservation effort on behalf of the environment and sustainable agri-food systems could encompass farmer education and extension assistance, financial assistance, cross-compliance measures, and compulsion backed by litigation and penalties. Such policies would best be targetted, especially when scarce public funds are earmarked for subsidizing farmers' conservation efforts, rather than universally applied. Targetting criteria should be not only high potential for achieving environmental protection and agri-food sustainability, but also positive net social welfare outcomes. Farm sites conferring highest positive net social welfare should be ranked first for targetting.
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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 | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".