Exploring Biological Variation and the Value of Natural History Collections Using an Online Lesson
Bibliographic record
Abstract
First-year science students in large-enrollment lecture courses are rarely given opportunities to contribute to science beyond their classroom as part of their curriculum. Meanwhile, natural history museums are eager to engage students and the general public in curation and research projects, but cannot risk damage to irreplaceable specimens and typically do not have the resources to manage volunteers on the scale of a large university course. One such museum is the Beaty Biodiversity Museum (BBM) at the University of British Columbia (UBC), Canada. The BBM is home to UBC’s natural history collections and contains over two million specimens, but, like any natural history museum, specimens are not physically accessible to the general public, including university students. This lesson was designed to be online, with only a short project introduction and wrap-up happening in class, in order to both protect specimens and allow large numbers of students to participate in a museum curation project. A set of readings and videos introduce students to biological diversity and how it is documented in natural history museums, in this case, an herbarium. Along the way, students complete three worksheet activities exploring (i) physical variation within a single species, (ii) how specimens are preserved and digitized, and (iii) how new scientific questions can be asked using digitized biodiversity data. During this lesson, students digitize herbarium specimen labels and make a meaningful contribution to science beyond their own classroom. A pre- and post-survey capture student knowledge and perceptions of biodiversity before and after the lesson. Primary Image: Two herbarium specimens of the bull kelp, Nereocystis luetkeana, demonstrating physical variation within a species. Images from the Consortium of Pacific Northwest Herbaria, used with written permission from Richard Olmstead, CPNWH Administrator.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".