Mapping Relationship Discovery of Multidimensional Architectures in Autonomous Transportation System Based on Text-Matching Model
Bibliographic record
Abstract
Mapping relationships of multidimensional architectures play an essential role in the autonomous transportation system (ATS), which can help understand the complex relationships between multidimensional architectures. The current mapping relationship discovery for multidimensional architectures in ATS requires significant manual involvement, leading to the underutilization of textual data and the intense subjectivity of results. In order to address the above issues, it is necessary to mine and further utilize the semantic information in the textual data. This study applies the text-matching model to the mapping relationship discovery of multi-dimensional architectures, which can calculate the semantic similarity between texts. On this basis, a method based on the Siamese-BERT-wwm-Bi-LSTM model is proposed, which incorporates Chinese BERT with whole word masking (BERT-wwm), bidirectional long-short term memory (Bi-LSTM), and the Siamese Network. A series of experiments are conducted with different text-matching models. The results show that the precision rate, recall rate, and F1-score exceed 80% for most applied methods, which verifies the feasibility of using the text-matching model for mapping relationship discovery. These results expect to provide a method with good performance that can automatically perform mapping relationship discovery.
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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 | 0.001 | 0.003 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".