Air voids measurement on asphalt mixes section surface based on image analysis
Bibliographic record
Abstract
Asphalt concrete is a porous and no uniform material; air void always plays an important role in asphalt concrete quality control. This study describes a new analytical method to quantify the air voids on section surface of asphalt mixes based on image analysis. This study establish a credible relationship of natural logarithmic between the micro index from this new measurement and traditional macro index of air voids and put forward a uniform method which can predict the percent air voids being suitable to various aggregates of asphalt concrete and easy to promote based on image analysis. This study also established a system approach to analyze the image features of air voids and defined the composition of the air voids features which are geometry, gray-scale and spatial distribution features. These image features of air voids can establish a quantized correlation with percent air voids or other macro index and can explain the phenomenon that some macro index conflict each other. It is necessary to study further into the micro level of air void properties; the image analysis technique provided in this paper will be a powerful too to study the features of air voids.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".