Depth Profilometry via Multiplexed Optical High-Coherence Interferometry
Bibliographic record
Abstract
Depth Profilometry involves the measurement of the depth profile of objects, and has significant potential for various industrial applications that benefit from non-destructive sub-surface profiling such as defect detection, corrosion assessment, and dental assessment to name a few. In this study, we investigate the feasibility of depth profilometry using an Multiplexed Optical High-coherence Interferometry MOHI instrument. The MOHI instrument utilizes the spatial coherence of a laser and the interferometric properties of light to probe the reflectivity as a function of depth of a sample. The axial and lateral resolutions, as well as imaging depth, are decoupled in the MOHI instrument. The MOHI instrument is capable of multiplexing interferometric measurements into 480 one-dimensional interferograms at a location on the sample and is built with axial and lateral resolutions of 40 μm at a maximum imaging depth of 700 μm. Preliminary results, where a piece of sand-blasted aluminum, an NBK7 glass piece, and an optical phantom were successfully probed using the MOHI instrument to produce depth profiles, demonstrate the feasibility of such an instrument for performing depth profilometry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".