Bibliographic record
Abstract
We explore the relationship between symmetry and entropy, distinguishing between symmetries of state and dynamical symmetries, and in the context of quantum thermodynamics between symmetries of pure and mixed states. Ultimately, we will argue that symmetry in thermodynamics is best understood as a means of control within the control theory paradigm, and we will describe an interesting technological application of symmetry-based control in the context of a quantum coherence capacitor. Symmetry, the concept from which Noether derived the conservation laws of physics, is one of the most important guiding principles of modern physics. Moreover, symmetry is often regarded as a form of order, and entropy is sometimes regarded as a measure of disorder, so it is natural to suppose that symmetry and entropy are related in some way. In this article, we will explore the relationship between symmetry and entropy, demonstrating that this relationship is by no means a simple one: in particular, it is important to distinguish between symmetries of state and dynamical symmetries, and in the context of quantum thermodynamics to distinguish between symmetries of pure and mixed states. Ultimately, we will argue that symmetry in thermodynamics is best understood as a means of control within the control theory paradigm, and we will describe an interesting technological application of symmetry-based control in the context of a quantum coherence capacitor.
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 | 0.002 | 0.003 |
| 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 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".