Atlas of the herpetofauna of Batna Province and the Belezma Biosphere Reserve, north-eastern Algeria
Bibliographic record
Abstract
Algeria, the largest country in Africa, exhibits significant gaps in knowledge regarding species’ geographical distributions, particularly for herpetofauna. This deficiency is attributed in part to the country’s vast geographical expanse, limited local engagement in herpetological research, and persistent underfunding for biodiversity studies. This study presents the first comprehensive atlas of the herpetofauna of Batna Province, northeastern Algeria, including the Belezma Biosphere Reserve, marking a critical step toward developing a national herpetofaunal atlas. Based on 12 years of field surveys and a critical review of historical records spanning nearly 150 years, we document 47 species (four amphibians and 43 reptiles), including seven new provincial records and five species not reported in Batna for over 130 years. Our findings indicate that Batna harbours over two-fifths of Algeria’s known herpetofaunal diversity, highlighting its significance as a biodiversity hotspot. This study also provides Arabic common names for the recorded species to enhance public engagement while offering insights into species richness distribution patterns, conservation, and biogeography. We believe this atlas addresses critical knowledge gaps and will contribute to more accurate biodiversity assessments, while informing effective conservation planning in Algeria and the Mediterranean region.
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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 | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".