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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".