Blind Source Separation of Photoacoustic Depth Profiles into Independent Components
Bibliographic record
Abstract
Step-scan photoacoustic spectroscopy is a powerful tool to nondestructively retrieve depth related information from a sample. Through digital signal processing a series of spectra with effectively different modulation frequencies, probing different thermal diffusion lengths within a sample, can be collected simultaneously. For layered samples spectra of the constituent layers can then be obtained by calculating spectra at specific phase angles from the in-phase and quadrature data through phase projection. However, without prior knowledge of the spectra of the constituent layers, this approach can be difficult. In this report we present an alternate possibility for evaluating step scan photoacoustic data, namely independent component analysis (ICA), which allows for ''blind separation'' of the mixed photoacoustic spectra without prior knowledge of the constituent spectra. Phase projection and ICA are applied to photoacoustic data acquired from a multilayer sample in an attempt to isolate the spectra of the constituent layers. The results for the two methods are comparable, with ICA offering the advantage that no prior information about the pure spectra of the sample layers is needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".