Habitat use by giant pandas (<i>Ailuropoda melanoleuca</i>) in relation to roads in the Wanglang Nature Reserve, People’s Republic of China
Bibliographic record
Abstract
The impacts of roads on wildlife and their habitats have been widely recognized. To assess the effects of roads on habitat use of the giant panda (Ailuropoda melanoleuca (David, 1869)), we investigated the giant panda habitats and the roadside habitats in Wanglang Nature Reserve, People’s Republic of China. We found that giant pandas did not use the road-affected habitats, and compared with giant panda habitats, road-affected habitats were characterized by lower bamboo density and grazing disturbances. Therefore, our study demonstrated that roads negatively affected the habitat use of giant pandas, and such affected habitats could not meet the needs of these animals. These results suggest that to minimize the negative effects of roads on the conservation of species, a full evaluation of the effects of roads on wildlife and their habitats should be conducted before road construction, and effective protection measures should be taken to control for these negative effects.
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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 | 0.001 |
| 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) | 0.000 | 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".