Molecular Weapons against Agricultural Vulnerability and the War on Terror
Bibliographic record
Abstract
The multiple reports in this issue of the Journal from the Agenda for Action conference, coupled with the analysis by the National Academy of Sciences, the National Research Council, and the Auditor General (UK) on bioterror preparedness and homeland security, highlight the immediate need for rapid disease detection and advanced diagnostic capabilities to protect the public health, animal agriculture, and the numerous associated economies in the United States. In response to the potentially devastating consequences that could arise, there is an acute need for rapid detection of a variety of the lethal foreign animal diseases, such as foot-and-mouth disease virus (FMDV), highly pathogenic strains of avian influenza, classical swine fever, rinderpest, exotic Newcastle disease virus (END), and domestic, vesicular look-alike diseases that include bluetongue, epizootic hemorrhagic disease, vesicular stomatitis, bovine herpes IBR, contagious ecthyma, bovine herpes mammilitis virus, vesicular exanthema, malignant catarrhal fever, and papular stomatitis. Some striking advances are occurring in the creation of rapid technology, including microfluidics, robotics, miniaturization, and biostabilization that are quickly being applied to the development of rapid microbial detection assays. These are now providing important weapons to combat this agricultural vulnerability.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".