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Record W4205344759 · doi:10.1515/ami-2020-0110

Caribou and reindeer migrations in the changing Arctic

2021· article· en· W4205344759 on OpenAlexaff
Kyle Joly, Anne Gunn, Steeve D. Côté, Manuela Panzacchi, Jan Adamczewski, Michael J. Suitor, Eliezer Gurarie

Bibliographic record

VenueAnimal Migration · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsYukon Department of EnvironmentGovernment of Northwest TerritoriesUniversité LavalCenter for Northern Studies
Fundersnot available
KeywordsArcticThe arcticGeographyClimate changePopulationEcologyBiologyOceanographyDemography

Abstract

fetched live from OpenAlex

Abstract Caribou and reindeer, Rangifer tarandus, are the most numerous and socio-ecologically important terrestrial species in the Arctic. Their migrations are directly and indirectly affected by the seasonal nature of the northernmost regions, human development and population size; all of which are impacted by climate change. We review the most critical drivers of Rangifer migration and how a rapidly changing Arctic may affect them. In order to conserve large Rangifer populations, they must be allowed free passage along their migratory routes to reach seasonal ranges. We also provide some pragmatic ideas to help conserve Rangifer migrations into the future.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.040
GPT teacher head0.358
Teacher spread0.318 · 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

Citations24
Published2021
Admission routes1
Has abstractyes

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