Why this work is in the frame
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Bibliographic record
Abstract
Abstract Algorithmic cooling (AC) is a recent spin‐cooling approach that employs entropy compression methods in open systems . AC reduces the entropy of spins on suitable molecules beyond Shannon's bound on the degree of entropy compression by reversible manipulations. Remarkably, AC makes use of thermalization, a generally destructive facet of spin systems, as an integral part of the cooling scheme. AC is capable of cooling spins to very low temperatures and provides significant cooling for molecules containing as few as 5–7 spins. Application of AC to slightly larger molecules could lead to breakthroughs in high‐sensitivity NMR spectroscopy in the near future. Furthermore, AC may be germane to the development of scalable NMR quantum computers. We introduce here a new practicable algorithm, “PAC3”, and several new exhaustive cooling algorithms, such as the Tribonacci and k ‐bonacci algorithms. In particular, we present the “all‐bonacci” algorithm, which appears to reach the maximal degree of cooling obtainable by the optimal AC approach. AC is potentially beneficial for NMR‐derived biomedical applications, which involve bio‐molecules with isotope enrichments, such as 13 C ‐ and 15 N ‐labeled amino acids. We briefly survey AC experiments, including a recent 3‐spin experiment in which Shannon's bound was bypassed. The difficulties associated with cooling molecules bearing a greater number of spins are explained. Finally, the potential of selected cooling algorithms (practicable, exhaustive, and optimal algorithms) is illustrated with regard to a highly relevant bio‐medical target— 13 C ‐labeled glucose.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it