Why this work is in the frame
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Bibliographic record
Abstract
Stations do have other challenges regarding capacity than open lines as it is here the traffic is dispatched. The UIC 406 capacity method that can be used to analyse the capacity consumption can be exposed in different ways at stations which may lead to different results. Therefore, stations need special focus when conducting UIC 406 capacity analyses.This paper describes how the UIC 406 capacity method can be expounded for stations. Commonly for the analyses of the stations it is recommended to include the entire station including the switch zone(s) and all station tracks. By including the switch zone(s) the possible conflicts with other trains (also in the opposite direction) are taken into account leading to more trustworthy results. Although the UIC 406 methodology proposes that the railway network should be divided into line sections when trains turn around and when the train order is changed, this paper recommends that the railway lines are not always be divided. In case trains turn around on open (single track) line, the capacity consumption may be too low if a railway line is divided. The same can be the case if only few trains are overtaken at an overtaking station. For dead end stations and overtaking stations, the dwell/layover time is recommended to be reduced to the minimum required time as it results in the lowest possible capacity consumption. For dead end stations it is furthermore recommended that the trains can use all possible tracks and not only those tracks they originally was assigned. For complex stations with shunting movement, the results of UIC 406 capacity analyses are imprecise due to different possible routes and no exact knowledge of shunting movements. For these stations it is instead recommended that they are analysed with a supplement to compensate for the inaccuracies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it