Parametrizing yields of nuclear multifragmentation
Bibliographic record
Abstract
We consider a model where, for a finite disintegrating system, yields of composites can be calculated to arbitrary accuracy. An analytic answer for yields is also known in the thermodynamic limit. In the range of temperature and density considered in this work, the model has a phase transition. This phase transition is first order. The analytic expression for yields of composites, in the thermodynamic limit, does not conform to the expression $〈{n}_{a}〉{=a}^{\ensuremath{-}\ensuremath{\tau}}f({a}^{\ensuremath{\sigma}}(T\ensuremath{-}{T}_{c}))$ where the usual identification would be that ${T}_{c}$ is the critical temperature and $\ensuremath{\tau},\ensuremath{\sigma}$ are critical exponents. Nonetheless, for finite systems, we try to fit the yields with the above expression. A minimization procedure is adopted to get the parameters ${T}_{c},\ensuremath{\tau},$ and $\ensuremath{\sigma}.$ While deviations from the formula are not negligible, one might believe that the deviations are consistent with the corrections attributable to finite particle number effects and might then conclude that one has deduced at least approximately the values of critical parameters. This exercise thus points to difficulties of trying to extract critical parameters from data on nuclear disintegration. An interesting result is that the value of ${T}_{c}$ deduced from the ``best'' fit is very close to the temperature at which the first order phase transition occurs in the model. The yields calculated in this model can also be fitted quite well by a parametrization derived from a droplet model.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".