The curation of Arachnida collections in alcohol: An international survey
Bibliographic record
Abstract
Abstract The Natural History Museum, London (NHMUK), has a large Arachnida collection in alcohol. Although the NHMUK follows best-practice in alcohol collections standards, there are still standards that have yet to be agreed upon, e.g., the choice of optimum printer and ink for producing labels. In 2012, I designed a survey to establish how alcohol collections are curated in other institutions around the world. I sent a questionnaire relevant to all collection sizes, materials, and storage spaces to 49 institutions in 36 countries. Responses from 42 institutions indicated: (1) collection size did not determine specific procedure; (2) museums with the largest collections are not restricted to one geographic region; (3) funding was the primary determinant of equipment and storage method, which sometimes resulted in unsuitable conditions; (4) although some methods were similar (e.g., use of ethanol), factors such as materials and equipment among other issues varied widely; (5) several issues are universal, and further research and the development of standards are needed. The results will be used to inform the establishment of further standards at the NHMUK and may also be a useful source of information for other institutions with alcohol collections. Current and future work on collection standards at the NHMUK is discussed.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".