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
The goal of this thesis is to provide algorithm design for smart vision sensors. Two algorithms are developed in this thesis. The first one is based on the correlation analysis method for images and can be implemented to find the 2-dimensional position of a target. In particular, the problem of finding the deviations along both X and Y directions is formulated as a matching process between the saved template, which represents the reference position, and the picture of a real static target captured by a 'vision' element such as a CCD camera, through correlation analysis of the 2-D spatial shift. It is an efficient and simple method for deviation identification of target position with high noise rejection ability. Using this method, 2-D position deviation can be found very accurately with high reliability. The second algorithm, which is based on locating the critical points of a planar shape, can be applied to identifying the pattern and finding the 2-dimensional position deviation and rotation angle of a target. (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .G36. Source: Masters Abstracts International, Volume: 40-06, page: 1586. Adviser: Xiang Chen. Thesis (M.A.Sc.)--University of Windsor (Canada), 2001.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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