Physics of particle collisions at high energies: Limits to phenomenology, Part 1
Bibliographic record
Abstract
Interpretation and understanding of high-energy PP data in a clear, consistent, and comprehensive manner is crucial for making valid claims to build up any successful theoretical framework for particle interactions. We have tried here to analyze the various sets of PP data available from the pre-ISR days to the latest PP collisions at the relativistic heavy ion collider (RHIC) experiment at [Formula: see text] = 200 GeV in the light of a power-law model. Both mid-rapidity and high-rapidity data sets have been dealt with by applying the same working formula. It is found that the working formula used provides a good description of these wide ranging data sets; but hardly throws any deep insights into the nature of particle interactions that force us to question the worth and rigour of phenomenological studies.PACS Nos.: 13.60.Hb, 13.60.Le, 13.85.Ni
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".