Evaluation of WIM data consistency based on temporal axle load spectra
Bibliographic record
Abstract
It is crucial to evaluate the consistency in weigh-in-motion (WIM) loading over time and quantify the relative accuracy of axle loads based on axle load spectra (ALS) data for different sensor types. This paper presents the temporal evaluation of the ALS from 51 WIM sites and 128 records available in the long-term pavement performance data. Analysis of ALS data over time shows that for single ALS, there is a significant difference in peak loads between the bending plate, and quartz piezo sensor measurements. Also, 100% of the bending plate 86% of the quartz piezo, and 66% of the piezo cables sites exhibited consistent single axle peak loads after 1 year of calibration event. For tandem ALS, significant differences were observed between loaded peaks of bending plate sensor from both quartz piezo and piezo cable sensors. Also, 83% of the bending plate, 78% of the quartz piezo, and 50% of the piezo cable sites exhibited loaded peaks consistently 1 year after calibration. The results show that calibration frequencies longer than 1 year may be acceptable for the bending plate sensors. However, calibration frequencies of at least 1 year for quartz piezo and less than a year for piezo cable sensors are recommended. Pavement designers and analysts should be aware of the changes in WIM data and calibration frequency over time.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".