Bibliographic record
Abstract
Soft materials, such as the filaments and membranes of the cell, may be subject to strong thermal fluctuations and sample a diverse configuration space. Each of these accessible configurations, of varying shape and energy, contribute to the ensemble average of an observable such as the mean length of a polymer chain. Statistical mechanics provides a formalism for describing the thermal fluctuations of a system; in this appendix, we review several of its cornerstones – entropy, the Boltzmann factor and the partition function – following a compact approach laid out in Reif (1965). As applications, the thermal fluctuations of a Hooke’s law spring and the entropy of an ideal gas are calculated. Temperature and entropy Any configuration available to a system, be it a polymer or a gas in a container, can be characterized by its energy, although more than one configuration may have the same energy. At zero temperature, the system seeks out the state of lowest energy, but at non-zero temperature the system samples many configurations as it exchanges energy with its environment. To determine the magnitude of these energy fluctuations, let’s examine two systems, L and S , in thermal contact only with each other, as in Fig. C1.1. System S is the subject of our observations, while L is the reservoir; L has far more energy than S . Because L and S exchange energy only with each other, their total energy, E TOT , is a constant : E TOT = E L + E S . (C1.1) We now specify E TOT to be a particular value E o sub>, say E TOT = E o = 10 6 Joules.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".