Materials in extreme environments for energy, accelerators and space applications at ELI-NP.
Bibliographic record
Abstract
As a leading facility in laser-driven nuclear physics, ELI-NP will develop innovative research in the fields of materials behavior in extreme environments and radiobiology, with applications in the development of accelerator components, new materials for next generation fusion and fission reactors, shielding solutions for equipment and human crew in long term space missions and new biomedical technologies. The specific properties of the laser-driven radiation produced with two lasers of 1 PW at a pulse repetition rate of 1 Hz each are an ultra-short time scale, a relatively broadband spectrum and the possibility to provide simultaneously several types of radiation. Complex, cosmic-like radiation will be produced in a ground-based laboratory allowing comprehensive investigations of their effects on materials and biological systems. The expected maximum energy and intensity of the radiation beams are 19 MeV with 10^9 photon/pulse for photon radiation, 2 GeV with 108 electron/pulse for electron beams, 60 MeV with 10^12 proton/pulse for proton and ion beams and 60 MeV with 107 neutron/pulse for a neutron source. Research efforts will be directed also towards measurements for radioprotection of the prompt and activated dose, as a function of laser and target characteristics and to the development and testing of various dosimetric methods and equipment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
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".