Assessment of Climatic Conditions for Siberian Reindeer Herding on the Basis of Heat Balance Modelling
Bibliographic record
Abstract
The purpose of the research is to assess suitable climatic conditions for traditional herding of reindeer by Indigenous people in different areas of Siberia. A сomputer simulation model allowed us to calculate reindeer’s heat balance according to a number of meteorological indices; it was used to assess climatic conditions in 70 localities. To show the impact of climatic conditions on reindeer’s well-being, we introduce the notion of the thermal comfort index (Kt). The best environmental conditions for reindeer are in the areas where Kt takes the highest values in winter and the lowest ones in summer. We showed the results of the reindeer heat balance computer simulation on two maps visualising the average Kt values in summer and in winter. Finally, using official statistics, we calculated the number of reindeer per 100 km2 in areas with different types of traditional reindeer herding. The territories with the largest domesticated reindeer populations per 100 km2 in the two major tundra reindeer breeding areas (Samoed and Chukchi-Koriak types of reindeer herding) are located in the regions with the relatively low value of Kt in summer and high in winter. In the taiga, Kt is relatively high in summer, and reindeer herding (Tungus and Saian types) is developed mostly in highlands, where the summer Kt is lower than in flatlands because of the vertical temperature gradient. The results obtained prove that thermal conditions are extremely important for traditional reindeer herding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".