Diode laser based photoacoustic gas measuring instruments intended for medical research
Bibliographic record
Abstract
Analysis of breath and gases emanated from skin can be used for early and non-invasive diagnosis of various kinds of diseases. Two portable, compact, photoacoustic spectroscopy based trace gas sensors were developed for the detection of methane emanated from skin and ammonia emanated from oral cavity. The light sources were distributed feedback diode lasers emitting at the absorption lines of ammonia and methane, at 1.53 μm and 1.65 μm, respectively. Photoacoustic method ensures high selectivity, therefore cross-sensitivity was negligible even with large amount of water vapor and carbon dioxide in the gas sample. In case of ammonia a preconcentration unit was used to achieve lower minimum detectable concentration. Gas sample from the oral cavity was drawn through a glass tube to the preconcentration unit that chemically bonded ammonia and released it when heated. The minimum detectable concentration of ammonia was 10 ppb for 15 s gas sampling time (gas sample of 250 cm3). For methane minimum detectable concentration of 0.25 ppm was found with 12 s integration time, and it was proved to be adequate for the detection of methane emanated from human skin and from mice. Instruments measuring methane and ammonia are currently installed at two medical research laboratories at University of Szeged and tested as instruments for non-invasive clinical trials. The aim of the measurements is to determine correlations between diseases or metabolic processes and emanated gases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".