Connecting tradition and technology: The digitization of the ethnobotanical collection at the Rio de Janeiro Botanical Garden
Bibliographic record
Abstract
Societal Impact Statement The digitization of RBetno (JBRJ) represents a step forward for biodiversity conservation in Brazil. Aligned with the Kunming‐Montreal Global Biodiversity Framework (Target 2, 2020–2030), this project documents the use of plants, including traditional knowledge and vernacular names, with a focus on the Atlantic Forest and Amazon. This initiative recognizes traditional communities, guardians of this ancestral knowledge, and creates an accessible biocultural repository. It allows new generations to reconnect with their heritage, strengthening memory and identity. This project demonstrates how technology can be utilized for biocultural conservation, providing data to inform future conservation policies and enhance the traceability of traditional knowledge. Summary The Rio de Janeiro Botanical Garden (JBRJ) maintains approximately 950,000 specimens in its collections, including the Ethnobotanical Collection (RBetno). These collections are important for understanding Brazilian biodiversity, supporting global conservation initiatives, and adhering to ethical guidelines for accessing traditional knowledge. As one of the largest herbaria in the Global South, the JBRJ prioritizes the digitization of its collection. This research focused on digitizing the RBetno to document traditional knowledge about plant use and vernacular names, particularly from the Atlantic Forest and the Amazon. This effort is considered essential for understanding Brazilian sociobiodiversity. The digitization process followed the protocols of the REFLORA Digitization Manual. The steps included data entry into the Jabot system, barcode generation and application, and high‐resolution image capture using full‐frame cameras (26–40 megapixels) with macro lenses, ensuring accurate detail. The digitized data were published according to GBIF (Global Biodiversity Information Facility) standards, making the information valuable for responsibly recording traditional knowledge, supporting research on plant use, and facilitating traceability and comparison with historical data. The digitization of the RBetno collection is essential for preserving traditional knowledge and fostering collaborative and intercultural research. The project also promotes awareness and education about Brazil's biocultural heritage.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".