Conservation priorities in Central Asia: the Shorsuv Massif IPA and its role in the Fergana Valley’s biodiversity
Bibliographic record
Abstract
Introduction The Fergana Valley (FV), a hotspot of endemicity and one of the most densely populated regions in Central Asia, faces increasing anthropogenic pressure. Methods In this study, geospatial conservation assessment and grid-based mapping of wild flora were integrated with traditional IPA identification methods recommended by Plantlife, resulting in a significant enhancement of the standard IPA approach. Results The Shorsuv Massif and its surrounding areas were identified as an IPA, meeting Criteria A and C of the IPA criterion developed by Plantlife. Given the unique biodiversity of the FV, the lack of IUCN Category I protected areas in the Uzbek part, and the increasing pressure of human activities, documentation and conservation of the plant diversity according to Plantlife criteria is of global importance. As a continuation of research in this direction, this paper details the identification of a third IPA in FV, located in the variegated outcrops of the Turkestan Range in the southwest of FV. Detailed field surveys and grid mapping documented 349 vascular plant species, including 42 threatened species under Criterion A of Plantlife International. However, the site and its surroundings are critically threatened by large-scale mining activities (Criterion C). Conclusions The first results of this study and their discussion with authorized representatives provides crucial data for informing the government’s decision to establish a new protected area in the FV. According to Decree No. PP-171 of the President of the Republic of Uzbekistan, dated 31 May 2023, a national park will be created, encompassing 100,000 hectares, including the Shorsuv IPA site and adjacent areas. This initiative also supports global conservation targets outlined in the Kunming-Montreal Global Biodiversity Framework (GBF) and the Global Strategy for Plant Conservation (GSPC).
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".