Depth Profiling of Optical Absorption in Thin Films via the Mirage Effect and a New Inverse Scattering Theory. Part II: Experimental Reconstructions on Well-Characterized Materials
Bibliographic record
Abstract
Mirage effect spectrometry is experimentally evaluated in this work as a technique of optical depth profiling in thin films where no prior information is available about the sample properties. An apparatus suitable for performing quantitative measurements is described. High-precision experimental alignment procedures are introduced along with a new method for precise optical correction of the detector signal for experimental frequency response nonuniformities. Reconstructions were made of the heat source density and absorption coefficient depth profile in materials with known depth dependence. These included samples approximating weighted delta function arrays, and depth-continuous media known to obey Beer's law to a good approximation. The properties of these samples were examined independently by using a technique of depth-sensitive light microscopy. Mirage effect depth profiles reconstructed on samples containing discrete absorbers were effectively regularization limited, indicating that resolution is limited by random error in the data rather than experimental bias. Depth profiles obtained in continuously absorbing media show a good agreement with those obtained by reference methods.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".