Intergeneration Division Based on Key Component Analysis in an Autonomous Transportation System Using the Natural Language Processing Method
Bibliographic record
Abstract
Advancement of emerging technologies and increasing of transport demands accelerate the evolution of the autonomous transportation system (ATS). Framework and architecture of ATS are becoming a research hotspot; however, by far, few studies on transportation intergeneration division are not basically involved. Previous works indicate that key components are critical representation in the distinguishing of long-term era. Besides, massive text material accumulates as the research work goes on, and natural language processing technique keeps developing, which makes quantitative research on key components in intergeneration division become possible. In this work, a method based on the massive text analysis is proposed. First, the LDA2vec is used to get the relationship between components and other elements. Then, a word set is from the component word set extraction module based on component items. Finally, the component word set is clustered to get ATS generation and to generate key components. Based on an analysis of large-scale important traffic texts, our method divides the traffic system into three generations for Chinese traffic from 2010 to 2022. The key components of our method given are consistent with human cognition of ATS. Successful application indicates that this work can be extended to other intergeneration division fields.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".