The Optimal method between SOR and LSs for impurities detection on physical materials
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
In recent years, image processing for detection has become an important field of research due to the increasing demand for it due to its many uses in modern technology.The aims of this study is to find alternative methods for image processing to achieve more accurate results with shortest time in term of detection.Therefore, the problem is finding an alternative mathematical method that can be converted into algorithms that interact with all the samples to be studied accurately and rapidly.To answer the problem, an experiment, using two different mathematical methods, was conducted via MATLAB software.The responses collected show that each method gets high accuracy in record time, especially the iterative method that is the Successive Over Relaxation one, which recorded the highest accuracy rates by 99.85%, and the Least Squares method by 90.95%.These results indicate the effectiveness of the two numerical methods used in this study and their superiority in terms of speed and accuracy.Further research should allow achieving more accurate results in a shorter period.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".