Operation Bone Rescue—A Case Study of Remediating Flood Damage to Mammal Specimens
Bibliographic record
Abstract
Abstract Water damage to natural history collections can result from both natural and human-caused environmental disasters. Floods can result in irreparable damage to scientific specimens, depending on the scale of the disaster, types of specimens affected, and availability of remediation resources. In April 2021, the mammal skeletal collection in the Biodiversity Research Collections (BRC) of the University of Connecticut (UConn) experienced a ceiling flood that affected 612 specimens. In this paper we detail all steps of our specimen rescue process and all materials and equipment we used to complete this remediation in an endeavor we termed “Operation Bone Rescue.” Because we were able to immediately respond to this emergency and implement a complete remediation plan, facilitated by funding from our university, we not only rescued all water-affected specimens, but also improved specimen storage and metadata. We highlight the holistic nature of this successful operation and the key roles played by personnel in the BRC, UConn Facilities Operations, Fire Department, and College of Liberal Arts and Sciences Dean's Office. A deep appreciation of the value of natural history collections is shared widely on our campus and resulted in the favorable outcomes of this complex, coordinated specimen rescue effort.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".