Bibliographic record
Abstract
Summary form only given. Although most laboratory plasmas are produced from heating of solids, little is known about the properties of the intervening states during evolution of a cold solid into hot plasma. Such states lie in the so-called Warm Dense Matter regime where temperature is comparable to Fermi energy and density is sufficiently high to render the ions strongly coupled. Experimental studies of Warm Dense Matter are challenging due to extreme pressure (~Mbar) of the states while theoretical studies are greatly complicated by the interplay of electronic excitation, electron degeneracy, and strong ion-ion correlation effects. Nonetheless, since its emergence in 1999 Warm Dense Matter has been rapidly gathering interest. This is driven by the fundamental significance of understanding the convergence of condensed matter and plasma physics as well as the relevance of Warm Dense Matter to broad areas including material science under extreme conditions, inertial confinement fusion, and planetary physics. Advances in Warm Dense Matter research are being propelled simultaneously by (i) ready availability of intense energy sources including lasers, free electron lasers, X-rays and energetic particles (electron and ion), and (ii) increasing capability in ab-initio molecular dynamic simulations. In this talk I will begin with a brief introduction to Warm Dense Matter. This will be followed by discussions on our earlier studies of electron-ion coupling, AC conductivity and solid-plasma transition in Warm Dense Matter states with energy density of ~1011J/m3.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.032 |
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".