Completeness of the amphibian inventory of Nayarit, Mexico, assessed from biodiversity information system records
Bibliographic record
Abstract
Climate change, diseases, pollution, and land cover changes threaten amphibian species worldwide, making identifying and monitoring these species crucial for the conservation of biodiversity. Nayarit, which is located in northwestern Mexico and within the Mexican Transition Zone and Neotropical biogeographic regions, hosts high amphibian diversity. However, information on the completeness of the amphibian inventory of Nayarit is lacking. Thus, the objective of the present study was to evaluate the inventory completeness of Nayarit using open-access biodiversity information systems. We constructed a database from the information stored in the Mexican National Biodiversity Information System (SNIB) of the National Commission for the Knowledge and Use of Biodiversity (CONABIO). Inventory completeness was analysed in 10-km cells and various periods, considering biogeographic and physiographic regions. Areas with high inventory completeness were defined by their proximity to the main communication routes rather than biophysical reasons. Approximately half of the state exhibited information gaps, especially in the mountainous and difficult-to-access areas. Additional studies are needed to fully document amphibian diversity in Nayarit, especially in geographical regions with few records. Our results provide a solid foundation for future research and are essential for adequately conserving and managing the natural resources of Nayarit.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".