Distribution of Hermann’s tortoise across Serbia with implications for conservation
Bibliographic record
Abstract
Hermann?s tortoise (Testudo hermanni) is among the conservation priorities in the European Union. Consequently, it is included in Annexes II and IV of the EU Habitats Directive, Annex II of the Bern Convention, and Annex II of the CITES Convention. Hermann?s tortoise conservation programs compile insights on the threats affecting population viability, along with factors shaping the species? distribution. Serbian populations of the eastern subspecies (Testudo hermanni boettgeri) seem numerous and therefore prosperous, but recent population viability analyses revealed that they are susceptible to rapid demographic changes and/or habitat destruction. This implies the need for effective population monitoring and protection, as well as mapping and preservation of suitable habitats. In this paper we summarized current knowledge about the geographic distribution of Hermann?s tortoise in Serbia and modeled its ecological niche. Our results corroborate and uphold the known species? distribution in Serbia. Most suitable habitats are situated in the lowland areas of eastern, central and southern Serbia, under semi-open habitats, such as pastures and shrubs, broadleaf forests, and all successional stages in between. The results provided in this paper should be considered in the selection and shaping of NATURA 2000 sites in Serbia. [Projects of the Serbian Ministry of Education, Science and Technological Development, Grant no. 173043 and Grant no. 173025]
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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".