Enzyme-Linked Immunosorbant Assays for Identification of Biological Agents in Sample Unknowns: NATO SIBCA Exercise IV
Bibliographic record
Abstract
In January 2002, the NATO Panel VII Subgroup on Sampling and Identification of Biological and Chemical Agents (SIBCA) conducted the fourth international training exercise on identification of biological agents. Fourteen NATO/Partners for Peace national laboratories participated: Austria, Bulgaria, Canada, Denmark, France, Germany (two laboratories), Italy, the Netherlands, Norway, Poland, Sweden, the United Kingdom, and the United States. The designated laboratory for Canada was Defence R&D Canada - Suffield (DRDC Suffield). Participant laboratories were sent six swabs. Participants were advised that samples would contain any one of the following gamma-irradiated organisms: Bacillus anthracis, Yersinia pestis, Brucella melitensis, Francisella tularensis, Vibrio cholerae, Burkholderia mallei, Venezuelan equine encephalitis (VEE) virus, vaccinia virus, Coxiella burnetii, or yellow fever virus. A number of immunologically-based technologies were used at DRDC Suffield for screening of sample unknowns, one of which was the enzyme-linked immunosorbant assay (ELISA). Antigen capture ELISAs were developed for all 10 possible biological agents and were used to screen the samples and a heterologous agent panel included as a control for specificity. Five biological agent unknowns were identified by ELISA, three at the species level: B. melitensis, F. tularensis, and Y: pestis, and two at the genus level: Bacillus spp. One sample containing V cholerae produced a false negative reaction. A comparison of the ELISA results with the identity of organisms in SIBCA sample unknowns, as revealed by Dugway Proving Ground following the exercise, indicated correct identification of three of the samples, a partially correct identification of two samples, and an incorrect false negative for one sample.
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 | 0.043 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".