Nomenclatural updating of the Manitoba Museum Herbarium
Bibliographic record
Abstract
Data from herbaria are used to assess the extinction risk of plants, and subsequently, create conservation plans. However, some research suggests that identification errors, including misidentification and use of outdated nomenclature, in herbaria are widespread. At the Manitoba Museum, nomenclatural updating of vascular plant specimens occurred from 1999 to 2001. However, due to staff time limitations, close examination of specimens did not occur. As a result, some misidentified specimens were not detected. In the 2010s, we began a second updating project, but this one both verified the identities, and updated names, of the vascular plant specimens in our collection. Of the 17 338 specimens of spore-producing plants, conifers, monocots, and dicots examined so far, nearly a third were either misidentified, or labelled with an out-of-date name. We describe the process that resulted in the correction of taxonomic errors through five case studies. We noted seven factors that influenced identification accuracy including (1) lack of staff time, (2) lack of good identification resources, (3) lack of expertise/familiarity, (4) poor specimen quality, (5) temporary inaccessibility of the collection, (6) superficial similarity of species, and (7) lack of technology. Changes are now being implemented at the Museum to help reduce identification errors in the future.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".