Precision of Florida Methods for Automated and Manual Faulting Measurements
Bibliographic record
Abstract
Traditionally, the Florida Department of Transportation (DOT) has measured faulting with a manual fault meter. However, this method is slow and labor intensive, disrupts traffic, and presents safety hazards. Extracting the fault magnitude from a pavement profile collected with an automated high-speed inertial profiler is a more efficient and cost-effective alternative. Thus, the Florida DOT developed the Florida automated faulting method (FAFM), which detects joints and calculates faulting from longitudinal profile data. A study was conducted to establish the accuracy and precision of the FAFM. In addition, an improved manual fault meter was developed by the Florida DOT and used as a reference device for the FAFM in the field. Because accuracy and precision measures for the new fault meter were not readily available, the study assessed the accuracy of the fault meter under controlled laboratory conditions, as well as the precision in both laboratory and field conditions. The results of the study indicated that the manual fault meter demonstrated no bias and a repeatability of 0.06 mm (0.002 in.) in laboratory conditions. Under field conditions, the fault meter showed a repeatability of 0.42 mm (0.02 in.). Also under field conditions, the FAFM achieved accuracy in terms of bias ranging between 0.2 mm (0.01 in.) and 0.7 mm (0.03 in.). The repeatability and reproducibility of the FAFM were determined to be 0.6 mm and 0.9 mm (0.04 in.), respectively.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".