Bibliographic record
Abstract
Heavy ion collisionsThe only practical way of creating and studying hot and dense strongly interacting matter in the laboratory is by colliding heavy nuclei at high energies.Some of the pioneering studies have used nuclear emulsion data of highly energetic cosmic ray events.However, a serious handicap there is the lack of control over the physical beam characteristics.For a few decades now, there has existed a vibrant experimental program seeking to explore the physics of nuclear collisions in different energy regimes and with different combinations of beam and target nuclei.The pioneering experiments at the Lawrence Berkeley National Laboratory (Berkeley, USA) have been followed by several other experimental ventures.It is impossible to enumerate all the facilities, but some important efforts at the high end of the energy spectrum have been pursued at the GSI (Darmstadt, Germany), CERN (Geneva, Switzerland), and at Brookhaven National Laboratory (Upton, USA).The Relativistic Heavy Ion Collider (RHIC) is located at BNL, and the Large Hadron Collider (LHC) has a heavy ion program expected to begin at CERN around 2007.A healthy experimental program in high energy nuclear collisions requires a basis in nucleon-nucleon and nucleon-nucleus collisions.These in fact constitute a crucial category of control experiments for the more complex nucleus-nucleus events.The study of strongly interacting matter at high temperature and density enjoys an active and fruitful collaboration between the experimental and theoretical communities.In relativistic nuclear collisions, multiple scatterings involving both the primary constituents (the original nucleons) and the secondary particles (mostly created pions) can, in principle, drive the system towards a state of local thermodynamic equilibrium.The reason for this originates in the phenomenology of hadronic collisions.From those studies it is known that, at energies relevant for the applications considered in this chapter, 317
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.287 | 0.177 |
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".