Aggregates & Agriculture: Cultivating neighbourly relations between critical industries
Bibliographic record
Abstract
Aggregate extraction and agriculture are important industries to rural Ontario. However, the high cost of transportation concentrates aggregate extraction into most municipalities peri-urban fringe bringing it into direct competition with agriculture for critical land resources. At the same time, the nature of both industries operations has the potential to create conflict on a day to day basis. While research has been conducted on the social impacts of aggregate extraction on rural communities, to date no comprehensive study of the aggregate industry effects on agriculture has been undertaken. This is in spite of the commonplace assumption that aggregate extraction negatively affects agricultural production. This multi-year project examines the experiences of multiple stakeholders, including aggregate producers, practitioners (ex. policymakers & consultants), and farmers working in close proximity to active aggregate sites, to understand this critical relationship and generate a set of best management practices to facilitate this relationship. Aside from land loss, no significant effects on agriculture have been reported; however, the presence of aggregate extraction on the rural landscape has the potential to create operational challenges for agriculture, especially in terms of road use and long-term land-use planning. Funding: OMAFRA through the Ontario Agri-food Innovation Alliance
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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 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".