Design of a New Diffractive Optical Element for Flattening the Gaussian High-Repetition-Rate Transversely Excited Atmospheric-Pressure CO2 Laser Beam Using an Iterative Angular Spectrum Algorithm through MATLAB
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
The editorial board announced this article has been retracted on December 21, 2012. If you have any further question, please contact us at: mas@ccsenet.org Article Title: Design of a New Diffractive Optical Element for Flattening the Gaussian High-Repetition-Rate Transversely Excited Atmospheric-Pressure CO2 Laser Beam Using an Iterative Angular Spectrum Algorithm through MATLAB Author/s: Alireza Heidari, Mehrnoosh Zeinalkhani, Abolhasan Nabatchian, Mohammad Amin Zare Soltani, Tahere Godarzvand Chegini, Mohammadreza Seifi Aghdam Malakabad & Mohammadali Ghorbani Journal Title: Modern Applied Science ISSN 1913-1844 (Print) E-ISSN 1913-1852 Volume and Number: Vol. 6, No. 5, 2012 Pages: 82-90 DOI: 10.5539/mas.v6n5p82 Mr. Mohammadali Ghorbani (Corresponding Author) formally apologizes to his dear and respected colleagues and journal editorial board. He gave deceiving them and got abuse their names, brilliant academic records, and reputations. His dear and respected colleagues have no knowledge or fault. He is a legal and moral responsibility to get this problem.
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.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".