Spend time now, save time later: IPM lessons learned from the National Museum of Natural History, Smithsonian Institution
Bibliographic record
Abstract
Abstract A case study involving a comprehensive inspection to discriminate between old and active pest infestations is described. Integrated pest management (IPM) processes within the National Museum of Natural History (NMNH), Smithsonian Institution, Division of Mammals (DOM) are challenging because of the size and composition of the collection, the age of storage equipment, and a low staffing to specimen ratio. Each specimen cabinet was inspected by IPM technicians during a 6-week period in late 2012. Following that inspection, two members of the NMNH collections program technician team began a 9-week project to clean 5,925 incidents in the affected cabinets in DOM storage areas in the Natural History Building downtown. The results of this project show that cleaning up a pest infestation in any natural history collection can be done in a reasonable amount of time and will help ensure the preservation of collections in the future. Knowing that the collections have been fully inspected and cleaned will allow staff in the DOM to easily and rapidly address future IPM issues in a structured way. Such efforts facilitate future IPM inspections because evidence of any new pest activity is no longer at risk of being overlooked due to debris from past infestations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".