Next‐generation specimen digitization: capturing reflectance spectra from the world's herbaria for modeling plant biology across time, space, and taxa
Bibliographic record
Abstract
Spectral reflectance measured from herbarium specimens represents a potentially vast source of information relevant to plant taxon identification and functional traits, which has inspired many laboratories world-wide to initiate next-generation spectral digitization from specimens. Combining these datasets into a coordinated global database would generate new capacity to model plant traits globally, enabling connection with remote sensing and ecological and biosphere models, as well as reconstruction of trait evolution. However, coordination is needed to avoid downstream problems in data aggregation due to variation in data standards and technical specifications of the instruments, optical setups, or measurement protocols. The International Herbarium Spectral Digitization (IHerbSpec) working group has initiated a globally collaborative program, outlining the central issues to address in establishing protocols, standards, and best practices, and proposing next steps. This collaborative effort will allow generation of replicable spectral reflectance data from plant specimens housed in herbaria around the world within ongoing digitization programs following community-defined standards and Findable, Accessible, Interoperable, and Reusable (FAIR) principles.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".