MétaCan
Menu
Back to cohort
Record W2951479547 · doi:10.1007/s13280-019-01205-x

Muskox status, recent variation, and uncertain future

2019· article· en· W2951479547 on OpenAlexafffund
Christine Cuyler, Janice E. Rowell, Jan Adamczewski, Morgan Anderson, John E. Blake, Tord Bretten, Vincent Brodeur, Mitch Campbell, Sylvia Checkley, H. Dean Cluff, Steeve D. Côté, Tracy Davison, Mathieu Dumond, Barrie Ford, Alexander Gruzdev, Anne Gunn, P. Hope Jones, Susan Kutz, Lisa‐Marie Leclerc, Conor D. Mallory, Fabien Mavrot, Jesper Bruun Mosbacher, I. M. Okhlopkov, Patricia E. Reynolds, Niels Martin Schmidt, Taras Sipko, Mike Suitor, Matilde Tomaselli, Bjørnar Ytrehus

Bibliographic record

VenueAMBIO · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsIsland HealthCenter for Northern StudiesUniversity of CalgaryUniversité LavalGovernment of NunavutYukon Department of EnvironmentMakivik CorporationMinistry of ForestsGovernment of Northwest Territories
FundersFaculty of Veterinary Medicine, University of CalgaryArctic Institute of North AmericaDirectorate for Biological SciencesEnvironment and Climate Change CanadaUniversity of CalgaryPinngortitaleriffik
KeywordsVariation (astronomy)Computer sciencePhysicsAstronomy

Abstract

fetched live from OpenAlex

Muskoxen (Ovibos moschatus) are an integral component of Arctic biodiversity. Given low genetic diversity, their ability to respond to future and rapid Arctic change is unknown, although paleontological history demonstrates adaptability within limits. We discuss status and limitations of current monitoring, and summarize circumpolar status and recent variations, delineating all 55 endemic or translocated populations. Acknowledging uncertainties, global abundance is ca 170 000 muskoxen. Not all populations are thriving. Six populations are in decline, and as recently as the turn of the century, one of these was the largest population in the world, equaling ca 41% of today's total abundance. Climate, diseases, and anthropogenic changes are likely the principal drivers of muskox population change and result in multiple stressors that vary temporally and spatially. Impacts to muskoxen are precipitated by habitat loss/degradation, altered vegetation and species associations, pollution, and harvest. Which elements are relevant for a specific population will vary, as will their cumulative interactions. Our summaries highlight the importance of harmonizing existing data, intensifying long-term monitoring efforts including demographics and health assessments, standardizing and implementing monitoring protocols, and increasing stakeholder engagement/contributions.

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)0.002
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.018
GPT teacher head0.222
Teacher spread0.204 · 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

Citations78
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueAMBIOSame topicClimate change and permafrostFrench-language works237,207