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Record W2513912492

High-tech satellite telemetry improves reindeer management on Alaska's vast rangelands.

2016· article· en· W2513912492 on OpenAlexaboutno aff
Jack Swanson, Greg Finstad, Randy Meyers, Karin L Sonnen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsGrazingGeographyRangelandPeninsulaRange (aeronautics)Circumpolar starArcticLivestockHerdFisheryPhysical geographyEcologyForestryArchaeologyOceanographyBiology
DOInot available

Abstract

fetched live from OpenAlex

R eindeer are an important livestock species in the circumpolar north. Currently there are approximately three to four million reindeer distributed across Russia, Scandinavia, Greenland, Iceland, Canada, and Alaska, with five hundred thousand animals slaughtered annually, producing over 20,000 metric tons of meat (Turi, 1998). In Alaska, the majority of commercially produced reindeer occurs on the Seward Peninsula and Bering Sea Islands. Currently on the Seward Peninsula (the largest contiguous grazing area in the state), 14 herds graze on 16,200,000 acres of available rangeland with individual range permit areas averaging 1,012,500 acres (Workman et al. 1991). These permitted grazing ranges are remote, bisected by large rivers and mountain ranges with few or no roads. Herders employ an extensive management scheme where reindeer are allowed to range freely. Most herders utilize boat, ATV, or foot travel to monitor and move their herds. This type of management is characterized by sporadic herder contact that often results in the herd being unsupervised for extended periods when overland travel is difficult, or during mechanical breakdown of all-terrain vehicles. Herders will often lose track of their animals and must extensively search for them when traveling conditions improve. Many animals, especially those co-mingling and migrating with the Western Arctic Caribou Herd (WACH), may leave permitted grazing areas and are permanently lost to the herder. Unsupervised grazing reindeer may not optimize use of range resources or may overgraze the range. For these reasons it is critical for reindeer herders to adopt new technology allowing year round monitoring of animal locations and evaluation of annual grazing patterns. During the past 25 years, Natural Resources Conservation Service and University of Alaska Reindeer Research Program have assisted Northwest and Western Alaska reindeer herders with range management and animal husbandry technology. Range assistance has centered on traditional inventories, ecological site mapping, similarity (condition), trend, and utilization assessments. Annually, Bureau of Land Management (BLM) resource specialists coordinate with NRCS to conduct range assessments with reindeer herders during the summer. These inventories have provided the baseline information for range conservation planning and management.

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 score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.323
Teacher spread0.303 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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