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Record W4406887093 · doi:10.1139/facets-2024-0084

Hopeful insights from wildlife recoveries in Canada

2025· article· en· W4406887093 on OpenAlexafffundvenueabout
Laurenne Schiller, Mathilde L. Tissier, Alexandra Davis, Clayton T. Lamb, Stefanie Odette Mayer, Allyson K. Menzies, René S. Shahmohamadloo, Karen J. Vanderwolf

Bibliographic record

VenueFACETS · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of CalgaryUniversité du Québec à MontréalUniversity of WaterlooCarleton UniversityDalhousie University
FundersLiber Ero Foundation
KeywordsWildlifeGeographyEnvironmental ethicsEcologyBiologyPhilosophy

Abstract

fetched live from OpenAlex

Facing the global biodiversity crisis, conservation practitioners and decision-makers seek to catalyze wildlife recoveries in their region. Here we examined social-ecological attributes related to threatened species recovery in Canada. First, we used a retrospective approach to compare the trajectories of the original species assessed by Canada’s species-at-risk committee and found that only eight of 36 species now have decreased extinction risk relative to the past. There were no significant differences in human or financial capacity provided for recovery across species doing better, the same, or worse; the only significant difference was whether the primary cause of decline was alleviated or not. Second, when looking at species assessed at least twice between 2000 and 2019 we found that only eight of 422 (1.9%) experienced both increasing abundance and decreasing extinction risk. The defining characteristic of successful recoveries was first alleviating the original cause of decline, which was most often accomplished through strong regulatory intervention. Once declines were halted, practical interventions were highly species-specific. It is instructive to learn from conservation successes to scale resources appropriately and our results emphasize the importance of threat-specific intervention as a fundamental precursor to the successful restoration of biodiversity in 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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

Citations1
Published2025
Admission routes4
Has abstractyes

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