Aggregates and Agriculture: Understanding the Impacts of Aggregate Production on Agriculture and Identifying Mitigating Strategies
Bibliographic record
Abstract
Across Canada, aggregate extraction provides economic stimulus for many rural locales, however these operations significantly alter the landscapes upon which they occur and are often considered a nuisance to adjacent land owners. Aggregate 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 extraction to help ensure that adjacent agricultural operations prosper, further protecting food security. This research identifies and explores the farm operator’s perspective concerning the impacts of aggregate extraction on crop and livestock production, along with corresponding management practices that can be utilized to mitigate these impacts. A review of literature, case study research, and key informant interviews illustrate potential social, economic, environmental, and land use impacts on agriculture and, as a result, food security. Examples from across the country and internationally provide insight into alternative management practices. Policies regulating aggregate extraction are also explored. The research conducted provides a framework to assist governments, land use planners, and aggregate operators in the management of the relationship between aggregate extraction and agricultural activity. Through the implementation of the identified best management practices, conflict and negative impacts to agricultural production and food security from aggregate operations across Canada can be reduced or mitigated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".