Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this work is proposed a new fully automated methodology using computer vision and dynamic programming to obtain a 3D reconstruction model of surfaces using scanning electron microscope (SEM) images based on stereovision. The horizontal stereo matching step is done with a robust and efficient algorithm based on semi-global matching. The cost function used in this study is very simple since the brightness and contrast change of corresponding pixels is negligible for the small tilt involved in stereo SEM. It is used a sum of absolute differences (SAD) over a variable pixel size window. Since it relies on dynamic programming, the matching algorithm uses an occlusion parameter which penalizes large depth discontinuities and, in practice, smooths the disparity map and the corresponding reconstructed surface. This step yields a disparity map, i.e. the differences between the horizontal coordinates of the matching points in the stereo images. The horizontal disparity map is finally converted into heights according to the SEM acquisition parameters: tilt angle, image magnification and pixel size. A validation test was first performed using as reference a microscopic grid with manufacturer specifications. Finally, with the 3D model are proposed some applications in materials science as roughness parameters estimation and wear measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it