Bibliographic record
Abstract
ii The border between classical physics and quantum mechanics has been puzzling physicists since the early days of quantum mechanics. There are some approaches to explain the Quantum-to-Classical transition e.g. the decoherence program. Here I stick to a recent approach which places the emphasis on the precision of measurements. I use this approach to explain why it is difficult to observe Schrödinger’s cat. To do so, I focus on a physical realization of Schrödinger’s cat which was reported in [4]. In this experiment, De Martini et al. amplify one photon of a singlet state to a macroscopic beam of light. I compare this Schrödinger’s cat to a system with pure classical correlation and show that if photon counting measurements on the amplified beam are coarse-grained, then the statistics of the system in De Martini’s experiment can be reproduced by a classical correlation. iii Acknowledgements First and foremost I offer my sincerest gratitude to my family, who has been supporting me for all these years and helped me feel safe when life was stormy. They have been always there to listen to my problems and to help me with their advices, supports and sometimes just with their pleasant smiles. I also would like to thank all those who helped me scientifically and professionally; my supervisor, Christoph Simon who helped me a lot with my project; Barry C. Sanders, who was my primary supervisor and helped me to find my way through my studies
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.510 | 0.298 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".