Plant resource availability and harvesting pressure in Khardung La, Ladakh
Bibliographic record
Abstract
Khardung La area (Ladakh, India) happens to be a hot spot for medicinal plant collection by locals and traditional healers but there is hardly any published information on the diversity and abundance of plants from this area. Such an information is a pre-requisite for conservation planning and future reference. We conducted detailed field surveys in and around Khardung La region to know the medicinal plant diversity and current levels of extraction by the local people and herbal healers. Information on vegetation parameters were collected across various habitats (land forms). Group discussions, informal meetings and questionnaire surveys were conducted with the plant collectors to assess the status of collection. Community structure of plants was assessed using standard vegetation sampling method. The study reveals that the area harbors more than 40 medicinal plants species and receives a large number of plant collectors each year. Collection is done mainly for personal use. However, commercial extraction was also evident. The collection method used by locals was destructive which might cause loss of diversity. Fifteen species had very low frequency due to their habitat specificity while 8 of them had very rare occurrence. Indicator Species Analysis (ISA) resulted in 4 communities in different habitats. Current methods and levels of harvesting pressure on various species around this area suggests that many species would soon become extremely rare. Hence, there is a need to generate awareness among the local stakeholders and encourage them to come up with sustainable harvesting practices and self-regulated collection regime so that commercial extraction is minimized.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".