Bibliographic record
Abstract
If traditional design method were adopted to design the head structure of high-speed train, lots of time would be taken and a large amount of workload be done and machining precision not be assured. After analyzing the characters of the design and the manufacturing of the head structure of highspeed train, the bend sheet-beam design software of the streamlined head structure of high-speed train was developed on the ARX of AutoCAD software and the Open I-deas of Ideas software flat roof, which is mainly based on the intersection theory between two Non Uniform Rational BSpline surfaces and 3-dimensional geometric modeling technique of solid. Grid structure made up of the bend sheetbeam ensures that the head structure design is carried through by the way of the loading structure tied in with the streamline shape, that the twisted beam is avoided to build, and the structure is provided with well technological characters of manufacture and assembly at the same time. Because the main edge of the bend sheet-beam is free-form curve, it can not been processed using NC machining tool directly. For every NC machining tool has its respectively character and its machining program is different from each other, according to the line approximation form of Non Uniform Rational B-Spline and the technique preferences of the NC machining tool, the numerical control machining software of the bend sheet-beam of the high-speed train head was presented based on the ARX of AutoCAD software flat roof. The machining problem of the bend sheet-beam is settled down. At present the both software are used to design the streamlined head structure of highspeed train in our country.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".