Elementary statistical models for collision-sequence interference effects
Bibliographic record
Abstract
In this paper a class of model suitable for application to collision-sequence interference is studied. In these models it is assumed that the intervals between collisions are constant rather than exponentially distributed, as would be true if the collision times formed a Poisson process. The model may be two dimensional or three dimensional. Velocities are assumed to be completely randomized in each collision. The distribution of velocities is assumed to be Gaussian, though use is not always made of that fact. As applied to vector collisional interference the models allow the evaluation of the effects of windowing, which is of importance for $\mathcal{N}$-body simulation of more physically accurate models. They also lead to estimation of the effects of infilling of the interference dip following from deviations of the induced dipole moment from the intermolecular force. As applied to scalar collisional interference the models show the existence of a hitherto unknown (albeit weak) correlation between immediately successive collisions. An extension to the models, in which the magnitude of the induced dipole moment is equal to an arbitrary power or sum of powers of the intermolecular force, allows estimates of the infilling of the interference dip by the disproportionality of the induced dipole moment and force. One particular such model leads to the most realistic estimate for the infilling yet obtained.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".