Assessing Variability of Surface Distress Surveys in Canadian Long-Term Pavement Performance Program
Bibliographic record
Abstract
In 1997 the Canadian Long-Term Pavement Performance (C-LTPP) project capitalized on an opportunity to have surface distress surveys completed twice at more than half of its test sections to provide some insight as to the variability between different raters. The major findings of that investigation are presented. Because only two distress surveys for each test section were available for comparison, traditional variance analysis techniques were not applicable. Instead, a technique involving an “agreement index,” also known as “Cohen’s weighted kappa statistic,” was used to directly compare the levels of agreement between the surveys. The kappa statistic considers the likelihood of chance agreement and also allows the introduction of penalty values for individual cases of disagreement. The analysis results indicated that the level of agreement based on crack type was very high; less agreement was observed for severity level. Agreement increased significantly with increased data aggregation, indicating that less variability will be present for network-level analyses than for project-level analyses. The effect of rater experience was also investigated, although no firm conclusions could be made with the available data. Recommended methods for reduction of the variability of future distress surveys included reduction of the number of severity levels from five to three, reduction of the number of individual agency raters, more frequent training of raters, and the use of rating teams. In general, the results of the C-LTPP project distress variability analysis were in agreement with previous studies, including the recent study completed by the U.S. LTPP program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".