An Alternative Shelving Arrangement for Natural History Collection Objects to Optimize Space and Task Efficiency
Bibliographic record
Abstract
Abstract A taxonomic and alphabetic arrangement (TAA) of objects on shelves has prevailed in fluid-preserved natural history collections while they were managed by scientists for their own research. Now most collections are databased and internet-accessible to facilitate very different forms of research accomplished remotely by researchers who require less physical access to specimens. The collections staff who make those data available struggle to manage collection growth with limited space and budgets, while demands on them are increasing, necessitating task and space-efficient collection management solutions. We describe an alternative arrangement of objects based on their size and catalog number (OCA) that capitalizes on modern databases. Our partial implementation of this system facilitated pragmatic between-system comparisons of space use and staff time required for routine tasks. Our OCA allows 17% more jars to be stored in a given space than a TAA (not counting spaces left for growth), but adjusting vertical spacing of shelves could increase that to 115%. Ten of 15 staff tasks were more efficiently accomplished in the OCA section of the collection, and we propose ways to improve efficiency for three of the four tasks for which the TAA outperformed the OCA.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".