Review of: "Computational substantial violation of the CHSH with close approximation of the respective quantum values"
Bibliographic record
Abstract
Potential competing interests: No potential competing interests to declare.This is an interesting paper, but I am not certain that it achieves the objectives that the author wants.A couple of format issues first.The author begins with a partial derivation of the CHSH inequality.Their demonstration is far from obvious but the inequality is so well known in the literature that I don't think a repeat demonstration of its derivation is necessary.The inequality could merely be stated, with a reference if desired.The author, in introducing their modification of the Glauber-Sudarshan representation, defines an alpha+ and an alphaparameter, and then states that alpha-= -alpha+.I think it would be simpler to just have the single parameter alpha with the appropriate sign change where needed.The distinction adds nothing to the argument and would simplify it for the reader.The author spends a great deal of time discussing the computer code used to implement their model.Unfortunately I am not fluent in R programming and cannot comment on the accuracy or validity of the code.I will accept that the author is competent and that the code implements their formulae correctly.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.012 |
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".