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Record W3112065938 · doi:10.2192/ursus-d-19-00005.2

Evidence for historical grizzly bear occurrence in the North Cascades, USA

2020· article· en· W3112065938 on OpenAlexaboutno aff
Kristin M. Rine, Anne M. Braaten, Jack Oelfke, Jason I. Ransom

Bibliographic record

VenueUrsus · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsGrizzly BearsUrsusGeographyMisinformationEcosystemEcologyPopulationBiology

Abstract

fetched live from OpenAlex

The North Cascades ecosystem of north-central Washington State (USA) and southern British Columbia, Canada, has been identified as 1 of 6 recovery zones for grizzly bears (Ursus arctos) that were at one time distributed across a nearly continuous range of western North America. The current small number of grizzly bears, along with an apparent scarcity of historical observations, obfuscates the extent to which the mountain range and its surrounding lowlands previously supported grizzly bears. We reviewed and synthesized what is currently known about the historical distribution of grizzly bears in and around the North Cascades to better inform possible future restoration actions. Archeological, ethnographic, and incidental evidence confirm the prehistoric and historic presence of grizzly bears in the ecosystem and surrounding lowlands. Successful implementation of grizzly bear restoration and management in the North Cascades is dependent in part on the perception that they are an integral component of the ecosystem's historical benchmark. Education and outreach efforts that focus on the influence of human perceptions and correcting misinformation about the history of bears in the ecosystem and their interactions with humans may improve long-term restoration success in the North Cascades.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.351

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)0.0000.000

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 head0.093
GPT teacher head0.277
Teacher spread0.184 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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