Enhancement of conservation knowledge through increased access to botanical information
Bibliographic record
Abstract
Herbarium specimens are increasingly recognized as an important resource for conservation science and virtual herbaria are making specimens freely available to a wider range of users than ever before. Few virtual herbaria are designed with conservation use as a primary driver. Exceptionally, Brazil's Reflora Virtual Herbarium (RVH) was created to increase knowledge and conservation of the Brazilian flora. The RVH is closely integrated with the Flora of Brazil 2020 platform on which Brazil's new national Flora is under construction. Both resources are accessible via the Reflora home page and thousands of users move seamlessly between these Reflora resources. To understand how the Reflora resources are currently used and their impact on conservation science, we conducted a literature review and an online survey. We searched for publications of studies in which Reflora resources were used and publications resulting from Brazilian researchers who were part of Reflora's research and mobility program. The survey contained multiple choice questions and questions that required a written response. We targeted Reflora webpage visitors with the survey to capture a wider range of Reflora users than the literature review. Reflora resources were used for a variety of conservation-relevant purposes. Half the 806 scientific publications in which Reflora was cited and 81% of the 1069 survey respondents accessing Reflora resources mentioned conservation-relevant research outputs. Most conservation-relevant uses of the Reflora resources in scientific publications were research rather than implementation focused. The survey of Reflora users showed conservation uses and impacts of virtual herbaria were more numerous and diverse than the uses captured in the literature review. Virtual herbaria are vital resources for conservation science, but they must document use and impacts more comprehensively to ensure sustainability.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".