Predicting the effectiveness of supplement time on delay recoveries: a support vector regression approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Investigating the effectiveness of supplement time is a critical method for dispatchers to understand the delay recovery capacity of railway sections and stations, thus improving real-time dispatching efficiency. Based on train operation data of the high-speed railway in China, a support vector regression (SVR) algorithm was employed to investigate the effectiveness of supplement times in railway sections and stations. First, the independent factors were determined, and the hyper-parameters of the SVR model were tuned with the operation data. Then, the performance of the predictive model was tested on the testing dataset. The results show that the predicted delay recovery cases of the model coincide highly with the actual cases. Additionally, the predictive performance of the model under allowable errors illustrates that the accuracy of the model can reach 95.96%, with a 1-minute allowable error. Finally, comparison analyses show that the proposed model outperforms other widely-used delay recovery models.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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