Particle yield fluctuations and chemical nonequilibrium in Au-Au collisions at<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msqrt><mml:mrow><mml:msub><mml:mi>s</mml:mi><mml:mrow><mml:mi mathvariant="normal">NN</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msqrt><mml:mo>=</mml:mo><mml:mn>200</mml:mn></mml:mrow></mml:math>GeV
Why this work is in the frame
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Bibliographic record
Abstract
We study charge fluctuations within the statistical hadronization model. Considering both the particle yield ratios and the charge fluctuations we show that it is possible to differentiate between chemical equilibrium and non-equilibrium freeze-out conditions. As an example of the procedure we show quantitatively how the relative yield ratio $\ensuremath{\Lambda}/{K}^{\ensuremath{-}}$ together with the normalized net charge fluctuation $v(Q)=\ensuremath{\langle}(\ensuremath{\Delta}Q){}^{2}\ensuremath{\rangle}/\ensuremath{\langle}{N}_{\mathrm{ch}}\ensuremath{\rangle}$ constrain the chemical conditions at freeze-out. We also discuss the influence of the limited detector acceptance on fluctuation measurements, and show how this can be accounted for within a quantitative analysis.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.432 | 0.005 |
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