AP4.5 - Detecting breath and skin emitted tracers of humans with flame-made sensor arrays
Bibliographic record
Abstract
Earthquakes are lethal natural disasters frequently burying people alive under collapsed buildings.Tracking entrapped humans from their unique volatile chemical signature with hand-held devices would accelerate urban search and rescue (USaR) efforts.Here, a compact and orthogonal sensor array has been designed to detect the breath-and skin-emitted metabolic tracers acetone, ammonia, isoprene, CO2 and RH, all together serving as sign of life.It consists of three nanostructured metaloxide sensors (Si-doped WO3, Si-doped MoO3 and Ti-doped ZnO), each specifically tailored at the nanoscale for highly sensitive and selective tracer detection along with commercial CO2 and humidity sensors.When tested on humans enclosed in plethysmography chambers to simulate entrapment, this sensor array rapidly detects tracers of human presence with low parts-per-billion (ppb) level accuracy and precision, unprecedented by portable detectors but required for USaR.These results were validated by bench-top selective reagent ionization time-of-flight mass spectrometry (SRI-TOF-MS).As a result, an inexpensive sensor array is presented that can be integrated readily into handheld or even drone-carried detectors for first responders to rapidly screen affected terrain.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".