Bibliographic record
Abstract
The sidewall stiffness of the Chinese Maglev train is so weak that it disagrees with the situation of passing each other in open air at high speed. The first problem of the structure optimal design to settle down is to enhance the sidewall stiffness. The car body weight was defined as the object variables, and the deflections and stresses in the sidewall were defined as state variables, and the board thickness of profiled extrusion material and composite plate were defined as design variables. Combined with finite element analysis technology, the optimal design of the maglev train body structure was carried out by mathematical programming approach method. The transversal displacement in the sidewall from load case 6 reduced from 3.8241mm to 3.2mm and from load case 7 reduced from 2.9153mm to 2.3531mm after 18 times iteration calculation optimized. The displacement value was decreased by 20%. The proposed method can be used to optimize the existing and new car body structure of domestic maglev train.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".