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Record W4251160329 · doi:10.3402/polar.v19i1.6538

Need and opportunity for a North American caribou knowledge cooperative

2000· article· en· W4251160329 on OpenAlexaff
Don Russell, Gary P. Kofinas, Brad Griffith

Bibliographic record

VenuePolar Research · 2000
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsYukon Department of Environment
FundersNational Science Foundation
KeywordsUngulateTraditional knowledgeIndigenousGeographySustainabilityMainlandKnowledge transferEnvironmental resource managementEthnobiologyEcologyKnowledge managementEnvironmental scienceBiologyComputer science

Abstract

fetched live from OpenAlex

The importance of migratory caribou (Rangifer tarandus) to northerners, the increasing pressure to extract non-living resources, and predicted global climate change have led researchers, managers and resource users alike to focus on how to improve our knowledge of this unique northern ungulate. Unprecedented threats to caribou sustainability, along with the increasingly acknowledged value of indigenous hunters’contribution to caribou research, pose the additional challenge to innovate research methods that accommodate differing cultural perspectives and facilitate communication among groups. This paper surveys the state of scientific knowledge of the eleven major northern mainland herds of North America. We recommend an approach to improve our working knowledge of barren-ground caribou in order to assess better future impacts. The transfer of knowledge gained from years of research and indigenous experience on many aspects of caribou ecology should be evaluated and, where applicable, transferred to herds with more modest databases. The establishment of a North American Caribou Monitoring and Assessment System, based on a synthesis of local knowledge and research-based science, is recommended as a tool for improved communication and collective learning.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.224
GPT teacher head0.523
Teacher spread0.299 · 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 designNot applicable
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

Citations2
Published2000
Admission routes1
Has abstractyes

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