VASCULAR PLANT SPECIES RICHNESS ALONG ELEVATION GRADIENT OF THE KARNALI RIVER VALLEY, NEPAL HIMALAYA
Bibliographic record
Abstract
In the present study, primarily we aim to check the prediction of species richness pattern with elevation gradient and compare the empirical study with regional pattern and regressed it with different environmental parameter as well. The sampling method was designed to include all the habitat types and vegetation. Latitude, longitude, altitude, and aspect were recorded for each plot. The total vascular plants along with the life forms were regressed against the altitude. The regression was also done between species richness and different environmental parameters. A Generalized Linear Model (GLM) with a quasi-poisson error of distribution was used to elucidate the pattern of species richness. A total of 199 vascular plant species were recorded of which 145 species were herbs, 21 trees and 33 shrubs. Species richness of total vascular plants and all life forms showed a unimodal pattern with altitude. An empirical study coincides with the regional study forming a peak at mid altitude but no plateau was observed in our study. Despite the pattern, regional studies showed a mid altitude peak at 1500 m asl but our study had a peak at an altitude of 3500 m asl. This study clearly shows the differences in pattern of species richness between the regional scale and local scale which is due to the differences in sampling strategy and data gathering methods.
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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 | 0.000 | 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) | 0.001 | 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".