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Threats to Endangered Species in Canada

2006· article· en· W2168416079 on OpenAlexafffundabout
Oscar Venter, N. N. Brodeur, Leah Nemiroff, Brenna Belland, I. J. Dolinsek, James W. A. Grant

Bibliographic record

VenueBioScience · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsConcordia UniversityUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsOverexploitationEndangered speciesThreatened speciesWildlifeHabitat destructionHabitatUmbrella speciesGeographyEcologyVulnerable speciesConservation-dependent speciesIUCN Red ListBiodiversityCritically endangeredBiology

Abstract

fetched live from OpenAlex

We quantified the threats facing 488 species in Canada, categorized by COSEWIC (Committee on the Status of Endangered Wildlife in Canada) as extinct, extirpated, endangered, threatened, or of special concern. Habitat loss is the most prevalent threat (84%), followed by overexploitation (32%), native species interactions (31%), natural causes (27%), pollution (26%), and introduced species (22%). Agriculture (46%) and urbanization (44%) are the most common human activities causing habitat loss and pollution. For extant species, the number of threats per species increases with the level of endangerment. The prevalence of threat types varies among major habitats, with overexploitation being particularly important, and introduced species particularly unimportant, for marine species. Introduced species are a much less important threat in Canada than in the United States, but the causes of endangerment are broadly similar for Canadian and globally endangered species.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.189
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations272
Published2006
Admission routes3
Has abstractyes

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