Classical‐Map Hypernetted Chain Calculations for Dense Plasmas
Bibliographic record
Abstract
Abstract Warm dense matter is of interest for modeling the interiors of planets and Brown Dwarfs. Corresponding pump‐probe experiments are performed at free electron laser facilities such as FLASH, LCLS or the future XFEL in Hamburg. X‐ray Thomson scattering is of special interest to extract the plasma parameters. In order to explain or predict the X‐ray Thomson scattering spectra, simulations on the structural properties of plasmas are performed. While ab initio simulations are computationally expensive, semi‐classical approaches can deliver results fast for pair distribution functions and static structure factors, even for dense systems. We solve the Ornstein‐Zernike equation within the hypernetted chain approximation for dense multi‐component plasmas using the classical‐map method. This approach proposes to treat the quantum features of the electrons using an adapted temperature for the electron system while the ions are treated classically. Results for pair distribution functions and static structure factors are presented for dense hydrogen, beryllium, carbon and CH plasmas. (© 2015 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".