Bibliographic record
Abstract
Abstract Quantum mechanics (QM) has led to spectacular technological developments, including the discovery of new constituents of matter and new materials: however, there is still no consensus regarding its interpretation and limitations. Some scientists and scientific writers promote some exotic interpretations and evoke quantum magic. In this paper we point out that magical explanations mean the end of the science. Magical explanations are misleading and counterproductive. We explain how a simple probabilistic local causal model is able to reproduce quantum correlations in Bell tests. We also discuss the difficulties of mathematical modeling of the physical reality and dangers of incorrect mental images. We examine in detail when and how a probabilistic model may completely describe a random experiment. We give some arguments in favor of the contextual statistical interpretation of QM. We conclude that we still do not know whether quantum theory provides a complete description of physical phenomena and we explain how it may be tested. We also point out, that there remain several open questions and challenges in QM, in quantum field theory and in the Standard Model. Moreover, there is still no consensus about how to reconcile quantum theory with general relativity and cosmology.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".