The struggle to share: experiences of revitalizing and digitizing a small scale herbarium
Bibliographic record
Abstract
Small herbaria have been recognized for the wealth of contributions to science held within their collections. Since the digitization of herbaria began, calls have been made to make small herbaria accessible through these efforts. However, various obstacles may prevent small herbaria from undertaking this work. In 2019, work began to revitalize and modernize the George F. Ledingham Herbarium at the University of Regina (approximately 70 000 specimens). The goals were to produce an electronic database, digitize the collection, make the data available through the online biodiversity platforms Canadensys and Global Biodiversity Information Facility (GBIF), and produce a herbarium-specific website. The initial focus has been on the Saskatchewan vascular plants, representing one-third of the herbarium’s accessions. To date, we have established a website, finished databasing the accessioned Saskatchewan vascular plant specimens, made the database available on the website, and completed 1700 scans. We are still working toward making our data available through online biodiversity platforms. Here, we share the positives and challenges that other small herbaria may face, detail approaches other small herbaria might take, and discuss the importance of digitizing and sharing herbaria collections through a freely accessible online platform.
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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 | 0.035 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.024 | 0.018 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 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".