Estimating tiger and its prey abundance in Bardia National Park, Nepal
Bibliographic record
Abstract
We estimated tiger and wild prey abundance in the Bardia National Park of Nepal. Tiger abundance was estimated from camera trap mark recapture in 85 days between December, 2008 to March, 2009 by placing 50 camera trap pairs in 197 trap locations with a sampling effort of 2,944 trap nights. We photo captured 16 individuals (≥1.5 year old) tigers identified on the basis of their unique stripe patterns. The number and density (per 100 km2) of tiger was 19 (SE 3.3) and 1.31 (SE 0.32), respectively. Distance sampling was used to assess the prey abundance on 170 systematically laid line transects between May–June, 2009. The density of all the wild prey (individuals/km2) was 56.3 (SE 6.5). The density (individuals/km2Banko JanakariA Journal of Forestry Information for NepalVol. 26, No. 1, Page: 60-69, 2016
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".