Species composition and habitat associations of birds around Jhilmila Lake at Western Chure Landscape, Nepal
Bibliographic record
Abstract
Abstract Wetlands support around 27% of birds in Nepal, however, there is a paucity of information about bird diversity and the wetland habitat of Western Chure Landscape Nepal. The “point count” method along transects was carried out to evaluate the species composition and habitat associations of birds. A total of 2,532 individuals representing 152 species (winter: N = 140 and summer: N = 91) from 19 orders and 51 families were reported from Jhilmila Lake and its surrounding area. The number of birds was reported to be significantly higher during winter than in the summer season. The species diversity was also higher in winter (Shannon’s index (H) = 4.38, Fisher’s alpha = 30.67) than in summer (H = 4.21, Fisher’s alpha = 34.69) as this area is surrounded by old-growth forest that provides available habitats for forest, grassland- and wetland-dwelling birds. This lake is an example of a wetland present in the Chure area that plays an important role in the conservation of biodiversity along with birds. Hence, we recommend its detailed study in terms of biodiversity and water quality.
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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 | 0.000 |
| 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".