A computerized data-capture system for animal biosafety level 4 laboratories.
Bibliographic record
Abstract
The restrictive nature of an Animal Biosafety Level 4 (ABSL4) laboratory complicates even simple clinical evaluation including data capture. Typically, clinical data are recorded on paper during procedures, faxed out of the ABSL4, and subsequently manually entered into a computer. This system has many disadvantages including transcriptional errors. Here, we describe the development of a highly customizable, tablet-PC-based computerized data-capture system, allowing reliable collection of observational and clinical data from experimental animals in a restrictive biocontainment setting. A multidisciplinary team with skills in containment laboratory animal science, database design, and software engineering collaborated on the development of this system. The goals were to design an easy-to-use and flexible user interface on a touch-screen tablet PC with user-supportable processes for recovery, full auditing capabilities, and cost effectiveness. The system simplifies data capture, reduces the necessary time in an ABSL4 environment, offers timely reporting and review of data, facilitates statistical analysis, reduces potential of erroneous data entry, improves quality assurance of animal care, and advances the use and refinement of humane endpoints.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.016 |
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".