High-tech satellite telemetry improves reindeer management on Alaska's vast rangelands.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".