Aggregate and Agriculture: Cultivating Neighbourly Relations, Stories from Across Ontario
Bibliographic record
Abstract
Aggregate extraction and agriculture are prominent land uses in rural southern Ontario, and both industries are vital contributors to the provincial economy. However, these industries compete for the same land base and their operations have the potential to negatively impact the other. There is currently little research into this relationship, particularly at the site or neighbour scale. This project, in its third year, is designed to address this gap and to provide best management practices to both agricultural and aggregate operators, as well as local and provincial governments, about how these industries can better work together. While research has been conducted regarding the social impacts of aggregate extraction on rural residents, little is known regarding the social, economic, environmental and land use impacts on farms operating in close proximity to aggregate extraction activity. The aggregate industry is widely believed to cause a variety of undesirable impacts, including noise, dust, road traffic, extended hours of operation, as well as a loss of water quantity and quality. The development of best management practices is important to help mitigate these potential impacts, both at the local level and for rural communities at large. This presentation provides a summary of research to date as well a preliminary analysis of more than 150 farm surveys collected over the last year. Next steps include further consultation with aggregate operators and more in-depth interviews with key informants from both industries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".