Bibliographic record
Abstract
BACKGROUNDn the past decade, all provinces except for Newfoundland have enacted some form of legislation to protect farm practices from nuisance litigation. 1 Such legislation is often referred to as 'right to farm' legislation.Manitoba enacted The Farm Practices Protection Act 2 in 1992 to protect farm practices from common law torts of nuisance.Under the Act, well-managed farms that do not pollute or threaten public health or safety are granted immunity from nuisance lawsuits.According to s. 2(1) of the Act, farm operations must not violate The Environment Act 3 , or The Public Health Act 4 , or a local land use control law.The Act also states that no nuisance action can commence until at least 90 days after the plaintiff has applied to the Farm Practices Protection Board for a ruling.5 Since right to farm legislation emerged, there have been no agricultural nuisance cases reported in Canada.6 In a private nuisance case, courts would have to decide whether the defendant's use and enjoyment of his land interfered with the plaintiff's reasonable use and enjoyment of their property.7 Such cases normally needed property damage, or non-material discomfort, annoyance, or 1 Jonathan Kalmakoff, "The Right to Farm: A survey of Farm Practices Protection Legislation in Canada" (1999) 62 Saskatchewan Law Review 225 at 226 [Kalmakoff].2
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".