Modeling Safe Motion Parameters of Transportation Modes Using Sensitivity Learning Method
Bibliographic record
Abstract
Different transportation modes have different motion parameters and different operating environments. Therefore,the safety levels are also different and influential factors are complex. Aiming at evaluating the operating safety of transportation modes,the sensitivity learning method based on random Markov chain was used to examine the correlations between the motion parameters and operating safety of transportation modes. Three modes studied include walking,bicycle and motor vehilce. According to typical motion parameters: maximum speed,acceleration,adaptation speed,minimum turning radius,et al.,this paper proposed the concept of sensitivity learning for these different modes of transportation and different motion parameters,and developed a model structure on transport parameters based on Markov processes between different parameters. This model structure can be used to evaluate the safety level of various modes of transportation and distribution of motion parameters. According to the reverse of the model,this paper obtains a certain security push motion parameters structure. These structures show that the people,who pursue high-speed travel,need to increase the impact of the acceleration and the corresponding minimum turning radiusto achieve a safe and reliable travel.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".