Micrometric Probe and Sample Position Measurement in Scanning Probe Microscopy Through Computer Vision
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Bibliographic record
Abstract
This work presents a computer vision-based methodology for precise, dynamic probe-sample distance measurement in scanning probe microscopy. The technique is tested by scanning a representative planar microwave probe and monitoring its position in three dimensions using a stereoscopic optical microscope. The results demonstrate that, through triangulation, the spatial resolution of the three-dimensional system surpasses that of the individual optical microscopes. This paper also introduces an approach that addresses the challenges of camera calibration and the limited number of feature matches between images, using augmented reality tags (ARTags) and Kalman filters to ensure process continuity and robustness. A notable feature of the methodology is its ability to estimate distances without relying on specific sample characteristics or direct contact with the sample surface. The proposed algorithmsare scalable, allowing for the generation of partial or complete reconstructions of micrometric.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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 it