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Bibliographic record
Abstract
The large set of accurate data on differential cross section and analyzing power from the CERN LEAR experiment on $\overline{p}\stackrel{\ensuremath{\rightarrow}}{p}{\ensuremath{\pi}}^{\ensuremath{-}}{\ensuremath{\pi}}^{+},$ in the range from 360 to $1550\mathrm{MeV}/c,$ is well reproduced within a distorted wave approximation approach. The initial $\overline{p}p$ scattering wave functions originate from a recent $\overline{N}N$ model. The transition operator is obtained from a combination of the ${}^{3}{P}_{0}$ and ${}^{3}{S}_{1}$ quark-antiquark annihilation mechanisms. A good fit to the data, in particular, the reproduction of the double-dip structure observed in the analyzing powers, requires quark wave functions for proton, antiproton, and pions with radii slightly larger than the respective measured charge radii. This corresponds to an increase in the range of the annihilation mechanisms, and consequently, the amplitudes for total angular momentum $J=2$ and higher are much larger than in previous approaches. The final-state $\ensuremath{\pi}\ensuremath{\pi}$ wave functions, parametrized in terms of $\ensuremath{\pi}\ensuremath{\pi}$ phase shifts and inelasticities, are also a very important ingredient for the fine tuning of the fit to the observables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.198 | 0.058 |
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".