Novel fluorescence-based integrated sensor for chemical and biological agent detection
Bibliographic record
Abstract
There is a renewed interest in the development of chemical and biological agent sensors due to the increased threat of weapons deployment by terrorist organizations and rogue states. Optically based sensors address the needs of military and homeland security forces in that they are reliable, rapidly deployed, and can provide continuous monitoring with little to no operator involvement. Nomadics has developed optically based chemical weapons sensors that utilize reactive fluorescent chromophores initially developed by Professor Tim Swager at MIT. The chromophores provide unprecedented sensitivity and selectivity toward toxic industrial chemicals and certain chemical weapon agents. The selectivity is based upon the reactivity of the G-class nerve agents (phosphorylation of acetylcholinesterase enzyme) that makes them toxic. Because the sensor recognizes the reactivity of strong electrophiles and not molecular weight, chemical affinity or ionizability, our system detects a specific class of reactive agents and will be able to detect newly developed or modified agents that are not currently known. We have recently extended this work to pursue a combined chemical/biological agent sensor system incorporating technologies based upon novel deep ultraviolet (UV) light emitting diodes (LEDs) developed out of the DARPA Semiconductor UV Optical Sources (SUVOS) program.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".