Protection des habitats d'especes menacees en terres priv:es: analyse d'instruments et de la politique canadienne
Bibliographic record
Abstract
The preservation of biodiversity requires the protection of endangered species’ habitats. In Canada, ap-proximately 60 percent of these habitats are located on private lands. We start by analysing the obstacles tothe protection of endangered species’ habitats, with special attention to the compensation of private prop-erty owners affected by conservation efforts. After briefly reviewing the main measures adopted in Canadato protect natural habitats on private lands, we propose some conservation mechanisms that would notexcessively burden public budgets. These measures should be discussed in the next proposal for a Canadianendangered species act.La preservation de la biodiversite necessite la protection des habitats des especes menacees. Au Canada,environ 60% de ces habitats sont situes sur des terres privees. Nous examinons la problematique de protec-tion de ces habitats et notamment la question de la compensation des proprietaires prives. Nous analysonsensuite les principales mesures utilisees au Canada pour preserver les habitats naturels, et nous proposonsdes mecanismes de protection qui permettraient de dedommager les proprietaires terriens affectes tout enlimitant les depenses publiques. Ces mecanismes devraient etre discutes lors de la prochaine proposition deloi sur la protection des especes menacees au Canada.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".