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Record W1504775844

Protection des habitats d'especes menacees en terres priv:es: analyse d'instruments et de la politique canadienne

2000· preprint· fr· W1504775844 on OpenAlexaboutno aff
Philippe Barla, Joseph A. Doucet, Jean‐Daniel Saphores

Bibliographic record

VenueRePEc: Research Papers in Economics · 2000
Typepreprint
Languagefr
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsnot available
Fundersnot available
KeywordsEndangered speciesHabitatGeographyForestryEnvironmental protectionHumanitiesEcologyEthnologyPolitical scienceHistoryBiologyArt
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.024
GPT teacher head0.298
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicEnvironmental Conservation and ManagementFrench-language works237,207