The big data analysis of rail equipment accidents based on the maximal information coefficient
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
With more electrical and electronic equipment applied into the railway system, much more data can be collected and then the big data era of railway is coming. By employing the maximal information coefficient (MIC), the big data analysis of rail equipment accidents is studied to investigate the effect of the updating of rail equipment. The rail equipment accident data set of 25 years (from 1990 to 2014) is separated into three subsets corresponding to the period of the occurrence time of accidents. For every subset, the contributing factors to accident damage, to accident severity, and to accident cause are analyzed, respectively. The results show that the variation trend of the number of rail equipment accidents is more consistent with the variety of railroad service miles rather than carloads. And the factor of highway-rail grade crossings is an important one which accords with the facts. However, a seemingly surprising result is found that there will be more contributing factors to accident severity and to accident causes with more equipment applied into the railway system as time goes on.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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