Summer habitat selection of reindeer ( <i>Rangifer tarandus</i> ) governs on the unprotected forest and human interface in China
Bibliographic record
Abstract
Abstract The habitat selection by animals depends on different environmental and anthropogenic factors such as the season, climate, and the life cycle stage. Here, we have presented the summer habitat selection strategy of reindeer ( Rangifer tarandus ) in the unprotected forest area from the northern arctic region of China. In summer 2012, we investigated a total of 72 used and 162 non-used plots in the reindeer habitat to record habitat variables. We found that the reindeer used significantly higher altitude, arbour availability, and vegetation cover area as compared to the non-used habitat variables. Principal component analysis (PCA) showed that six principal components (68.5%) were mainly responsible for the summer habitat selection of reindeer such as the slope position, concealment, anthropogenic dispersion, arbour species, distance from the anthropogenic disturbance area (> 1000 m) and water quality (Wilks’ Lambda = 0.12; P = 0.0001). The local people are largely dependent on forest product resource in these regions, such as bees herding, collecting wild vegetables, hunting, poaching, and grazing. These activities highly influenced the reindeer habitat and its behaviours. This study thus confirmed that reindeers are forced to choose poor habitat in unprotected forest area with high human disturbance or interference. These factors should be considered by the concerned authority or agency to manage reindeer population in the wild.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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 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".