Bibliographic record
Abstract
The question of quantifying the sharpness (or unsharpness) of a quantum mechanical effect is investigated. Apart from sharpness, another property, bias, is found to be relevant for the joint measurability or coexistence of two effects. Measures of bias will be defined and examples given. Dedication The impossibility of measuring jointly certain pairs of observables is an intriguing non-classical feature of quantum theory that Pekka Lahti identified as a candidate for a rigorous formulation of the principle of complementarity. While he was investigating this fundamental no-go statement in the early 1980s, he learned from Peter Mittelstaedt that one of his students was aiming to prove the positive possibility of approximate joint measurements of complementary quantities such as position and momentum. Pekka joined our group as an Alexander von Humboldt Fellow, and together we found that a reconciliation between complementarity and (approximate) joint measurability is possible on the basis of the generalized representation of observables as positive operator measures (POMs). Since then we have pursued together our aspirations of understanding quantum mechanics and understanding Nature. I have benefited much from Pekka’s intellectual rigor and have been privileged ever since to enjoy his warm humanity. It is a great pleasure to present this paper to Pekka as a token of thanks and friendship on the occasion of his 60th birthday, with all good wishes for many happy recurrences and productive years to come. 1
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".