White-rumped Vulture’s Habitat Suitability Prediction using MaxEnt in Arunachal Pradesh
Bibliographic record
Abstract
Few reports showed that White-rumped vulture is present in Arunachal Pradesh. However, they were reported from a few places only. Such sightings suggest that either the region is not explored completely or the habitats are not suitable for the species. Therefore, knowing and predicting the habitat suitability of WRV and revealing the relative contribution of environmental variables determining such distribution can be important for their protection and conservation. The present study was based on the current distribution of WRV in Arunachal Pradesh that we had surveyed from 2016 to 2020. We followed the road count and point count methods to obtain primary occurrence data. Also, secondary data on occurrence records and data on environmental variables (landscape variables, anthropogenic variables, and climatic variables) were obtained and used. The data were processed using ArcMap. 29 occurrence records (filtered) and 11 environmental variables were used to build the prediction model using maximum entropy (MaxEnt). The MaxEnt predicted model showed high accuracy with area under the receiver operating characteristic curve value equals to 0.95 and True Skill Statistics value equals to 0.87. Of the total area, only 2629.63 km2 (3.20 %) is suitable for WRV while the majority of the area is unsuitable (79542.84 km2) (96.79 %). The elevation (32.2%), land use land cover (31.7%), and normalized difference vegetation index of November (26.7%) were the most influencing variables impacting the distribution of WRV. Among bioclimatic variables, the mean temperature of the warmest quarter and precipitation of the wettest quarter had the highest contribution. This work is the first attempt to understand the spatial distribution of WRV and the environmental factors associated with their distribution in the state. The findings can be relevant for designing conservation efforts to conserve this species in the state.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".