Reptiles of Chubut province, Argentina: richness, diversity, conservation status and geographic distribution maps
Bibliographic record
Abstract
An accurate estimation of species and population geographic ranges is essential for species-focused studies and conservation and management plans. Knowledge of the geographic distributions of reptiles from Patagonian Argentina is in general limited and dispersed over manuscripts from a wide variety of topics. We completed an extensive review of reptile species of central Patagonia (Argentina) based on information from a wide variety of sources. We compiled and checked geographic distribution records from published literature and museum records, including extensive new data from the LJAMM-CNP (CENPAT-CONICET) herpetological collection. Our results show that there are 52 taxa recorded for this region and the highest species richness was seen in the families Liolaemidae and Dipsadidae with 31 and 10 species, respectively. The Patagónica was the phytogeographic province most diverse in species and Phymaturus was the genus of conservation concern most strongly associated with it. We present a detailed species list with geographical information, richness species, diversity analyses with comparisons across phytogeographical provinces, conservation status, taxonomic comments and distribution maps for all of these taxa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| 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) | 0.004 | 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".