Understanding the Impacts of Aggregate Production on Agriculture and Identifying Mitigating Strategies
Bibliographic record
Abstract
Across Ontario, aggregate extraction provides economic stimulus for many rural locales, but these operations significantly alter the landscapes upon which they occur and are often considered a nuisance to adjacent land owners. Especially in Southern Ontario, these operations frequently occur on agricultural land or within close proximity to productive farmland. Given the potentially disruptive nature of aggregate extraction, it is important to understand their impacts on nearby farms so that measures to mitigate these impacts can be developed and implemented. Thus, research is needed that understands the social, economic, environmental and land use impacts of aggregate operations to help ensure that adjacent agricultural operations prosper. This research therefore seeks to identify the farm operator’s perspective on impacts on crop and livestock production, along with corresponding best practices that can be utilized to mitigate these impacts. Additionally, this project will involve a jurisdictional scan to identify social, economic, environmental and land use impacts, as well as quantitative and qualitative research intended to identify impacts on agriculture (such as dust, noise and water) and promising practices that aggregate operators and municipal planners could use to limit these impacts. The goal is to see these best practices implemented early in the planning process to avoid conflict and negative impacts on agricultural production from future aggregate operations. The project is supported by a three-year research grant from OMAFRA.
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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 | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".