Tracking changes in natural history collections utilization: A case study at the Museum of Southwestern Biology at the University of New Mexico
Bibliographic record
Abstract
Abstract Natural history collections (NHCs) are used in many fields of study, but general knowledge regarding their uses is poor. Because of this, funding and support for NHCs frequently fluctuate. One way in which collections professionals can illustrate a collection’s contribution to a variety of fields is based on the collection’s history of use. Tracking NHC utilization through time can increase NHC value to others outside of the collection, allow for the analysis of changes in specimen-based research trends, and assist in effective collection management. This case study focuses on NHC usage records held by the Museum of Southwestern Biology (MSB), a currently growing university collection used in many research fields, and presents methods for quantifying collections utilization through time. Through an exploration of these data, this paper illustrates MSB’s growth and changes in research produced over time and offers explanations for the changes observed. Last, this study provides suggestions for how collections professionals can most greatly benefit from considering NHC records as a data source. Understanding NHC usage from “the collection’s perspective” provides a new way for NHC professionals to understand NHCs’ value in the context of the research it supports and demonstrates the importance of this key infrastructure to a broader audience.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".