Wildlife road-kills on the Tikauli section of East-West Highway in Barandabhar Corridor Forest, Chitwan, Nepal
Bibliographic record
Abstract
Roads are one of the linear infrastructures that play important role in nation development. Roads create barrier for the movement of wildlife, however, their impacts on wildlife are not sufficiently studied in Nepal. Thus, current study attempted to explore the impacts of Tikauli section of East-West Highway of Nepal on the wildlife of Barandabhar Corridor Forest (BCF). Wildlife vehicle collisions (WVCs) were recorded from December 2019 to September 2020 by dividing a day into morning, day, and late evening periods. Primary data were collected through direct road survey and key informant interview (n = 22) whereas secondary data were collected from the annals of Chitwan National Park, National Trust for Nature Conservation-Biodiversity Conservation Center and Division Forest Office, Chitwan, Nepal. Arc GIS 10.5 was used to produce relevant illustration and WVC hotspot identification based on Kernel Density Function. Out of thirty-three dead animals observed during the study period, spotted deer (Axis axis) were killed most frequently (n = 11) from WVCs followed by the Oriental garden lizard (Calotes versicolor). The highest number of deaths were recorded in winter and in the late evening. Besides keeping track of WVC records properly, further research is recommended.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".