Using online databases to produce comprehensive accounts of the vascular plants from the Brazilian protected areas: The Parque Nacional do Itatiaia as a case study
Bibliographic record
Abstract
BACKGROUND: Brazil is one of the most biodiverse countries in the world, with about 37,000 species of land plants. Part of this biodiversity is within protected areas. The development of online databases in the last years greatly improved the available biodiversity data. However, the existing databases do not provide information about the protected areas in which individual plant species occur. The lack of such information is a crucial gap for conservation actions. This study aimed to show how the information captured from online databases, cleaned by a protocol and verified by taxonomists allowed us to obtain a comprehensive list of the vascular plant species from the "Parque Nacional do Itatiaia", the first national park founded in Brazil. All existing records in the online database JABOT (15,100 vouchers) were downloaded, resulting in 11,783 vouchers identified at the species level. Overall, we documented 2,316 species belonging to 176 families and 837 genera of vascular plants in the "Parque Nacional do Itatiaia". Considering the whole vascular flora, 2,238 species are native and 78 are non-native. NEW INFORMATION: The "Parque Nacional do Itatiaia" houses 13% of the angiosperm and 37% of the fern species known from the Brazilian Atlantic Forest. Amongst these species, 82 have been cited as threatened, following IUCN categories (CR, EN or VU), seven are data deficient (DD) and 15 have been classified as a conservation priority, because they are only known from a single specimen collected before 1969.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".