Bringing an Historic Collection into the Modern Era: Curating the J. K. Underwood Seed Collection at the University of Tennessee Herbarium (TENN)
Bibliographic record
Abstract
Abstract The University of Tennessee Herbarium (TENN) presents a case study for modernizing an historic seed collection. TENN staff recently rediscovered the J. K. Underwood Seed Collection (ca. 1931–1964), containing over 700 unique specimens, hidden away in storage. We employed a series of curation actions to modernize the collection and render it useful to researchers. This included physically organizing and digitally indexing the collection, updating scientific names to current taxonomy, storing the specimens in modern archival-quality containers, housing the collection in environmentally-controlled conditions, and increasing accessibility of the collection by photographing specimens and integrating these images into our existing website (tenn.bio.utk.edu). Our efforts also included developing a protocol for adding new accessions to the collection and advertising the utility of the collection as a source of morphological data on seeds for identification, research, and teaching. We also review modern strategies for curating seed collections. Specifically, we emphasize the importance of increasing visibility of collections through visual, digital representations. This expands the utility of collections and fosters global information sharing across disciplines. We present our curation project as a case study that can serve as a model for curating historic seed collections.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".