Reducing Conflict between Rural Residential Developments and Hog Operations: A Decision Support Tool for the Selkirk and District Planning Area, Manitoba
Bibliographic record
Abstract
In certain rural areas of Manitoba, the character of the rural residential population has changed. People have built or bought houses around land that had been previously used exclusively for agriculture. These rural residents have invested in their property and are very sensitive to any activity that may interfere with their “rural lifestyle” or affect the value of their property. In the past, livestock production, in particular hog production was generally one component of mixed farming operation. Livestock production in Manitoba has undergone significant changes in recent years, both in size of operation and production method. It has now become a specialized industry where operations have become much larger and more capital intensive than farms of thirty years ago. These factors have resulted in situations where land use conflicts have and continue to occur. Typically, regulatory zoning, in conjunction with manual review of land cover overlay and topographic maps have been used to select sites for livestock operations. This approach can be time consuming and expensive. An alternative approach is the development of a geographic information system (GIS) to define optimal locations for livestock operations and non-farm rural residents. The use of such a model has the capability to reduce the number of rural land use conflicts. This study starts by documenting the significant changes in recent years of rural residential development and the size as well as the production method of hog operations in Manitoba. It then draws on a series of interviews to gain insight into the complex land use conflicts within the study area and to inform the creation of a geographic information system (GIS) model. This practicum explores “smart” land use analysis using a combination of GIS and Land Use Conflict Identification Strategy (LUCIS) modeling to represent the spatial consequences of land use decisions. This research has resulted in the development of a GIS model to be used as a decision support tool in developing policy surrounding future development and land use; including appropriate locations of any new or expanding livestock operations and rural non-farm residents within the Rural Municipality (RM) of St. Andrews, MB.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".