Processing and digitizing a nonstandard herbarium collection: a cautionary tale about research value
Bibliographic record
Abstract
Many private and nonstandard natural history collections have been discarded or denied inclusion in herbaria due to a lack of precise documentation or unusual preservation methods. This exclusion is often enabled by limited capacity for special projects and a lack of expertise or paid staff within herbaria to process such materials. As a result, despite an overall increase in digitized specimen data through time, valuable information remains omitted from these biodiversity datasets, which may be lost forever, unless strategies to incorporate them are developed. Using one nonstandard collection as a case study, we demonstrate how curators might assess, process, and digitize such collections, making them accessible to a wider audience, while preserving their original “character”. Through this example, we review a few ways to quantify research value within these types of collections, highlighting rare and endangered species, as well as extensions to geographic ranges or temporal coverage of otherwise known populations. We show that rather than discarding these specimens, with careful attention to assessment and processing, they can be efficiently cataloged and made usable by researchers, while maintaining the integrity of the collector’s “personality” and the unique story behind each collection.
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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 | 0.063 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".