Quality Control and Management in Data Production Process of High-speed Railway Settlement Observation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High smoothness and high stability are two most important characteristics in High-speed railway, which determine the significance and importance of settlement and deformation monitoring of underline engineering. The settlement observational data is complex and massive, only by reasonable and effective quality control and management, can we ensure high-speed railway’s be successfully constructed. This article sets up a strict quality control and management measures through disassembleing the procedure of settlement observational data production, to make the procedure standardized and institutionalized, and ensure the settlement observation successful implementation. Meanwhile, establish a timely quality information feedback mechanism of settlement observational data, achieve reliable and controllable settlement observation, and improve the production quality of settlement observational data to ensure high-speed railway’s construction.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it