Effective Normalization Method for Sample-Position-Dependence Effect in Photoacoustic Spectrometry
Bibliographic record
Abstract
Sample position dependence effect in photoacoustic (PA) spectrometry has been reported by several scientists. This effect must be taken into account in a PA application that requires a quantitative theoretical treatment. In this work, we experimentally investigated PA signal magnitude varying with sample-to-window distance in an MTEC Model 300 Photoacoustic Detector, which has a fixed empty (gas) volume in addition to the sample-to-window-distance-dependent gas volume. An operative method was introduced to obtain the coefficient, which considered the sample-to-window distance and the additional gas volume. With this coefficient, the one-dimensional PA model, developed by Aamodt, Murphy, and Parker, can be employed to quantitatively process PA experimental data, no matter what the sample-to-window distance is. Quantitative measurements of thermal effusivities of two samples were performed to prove this effective normalization method.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".