Image Requirements for Three-Dimensional Measurements of Pavement Macrotexture
Bibliographic record
Abstract
This paper examines the effect of wavelength range on the estimation of pavement macrotexture quality from a three-dimensional (3-D) surface model. Macrotexture indicators computed from two-dimensional (2-D) profiles are compared with power spectrum energy computed from 3-D surface heights in the frequency domain. Pavement samples with different types of surface conditions were evaluated by means of a noncontact photometric stereo system. For each pavement sample, surface heights were recovered and a 3-D surface model was constructed. Surface profiles were also measured manually with a dial gauge. The surface heights in frequency domain were divided into 10 wavelength ranges. Power spectrum energy was computed for each wavelength range. The correlation between the 2-D indicators (mean profile depth and root mean square roughness) and the power spectrum energy was examined. It was found that the texture indicators computed from the 3-D recovered surface could represent the 2-D indicators adequately. Moreover, the power spectrum energy provided a good estimation of the 2-D indicators when it was computed from wavelength ranges of approximately 13 times the expected range of the 2-D indicators.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".