Development of CPATT Database and Quality Control Checks for Concrete Pavement Field Data
Bibliographic record
Abstract
Abstract \n \nThe Centre for Pavement and Transportation Technology (CPATT) located at the University of Waterloo, Canada, has robust research capabilities. This is largely related to a team of researchers who have conducted a number of research(s) related to transportation and pavement engineering in collaboration with various public and private sector partners. Numerous research has involved collection, acquisition and development of new data and information involving design, construction, maintenance, economic, rehabilitation, safety and impact of climatic changes on the pavement. \n \nThe CPATT researchers collected data has a magnitude of multi-gigabytes. This required that an appropriate repository is provided and maintained for future students, researchers, and research partners. The repository qualifications and its maintenance process are detailed in this study. \n \nAt the start of this research, there was a detached data repository and a framework for collecting, storing and maintaining the database. This research has provided solutions to form the basis of a robust, meaningful and useful database, by researching and developing a repository, creation of numerous standard formats for datasets, inter-relationship models and quality control checks. The research has evolved so that concrete pavement field data can be stored safely and accessed by students, and researchers for analysis and its utilization in the future. \n \nIn developing a database framework, the literature indicates that an appropriate consultation with experts and rigorous evaluation of database framework (before its implementation) is to be carried out to meet the objectives and goals of the program. This objective was achieved by consulting CPATT management, IT experts (both internal and external to the University of Waterloo) and end-users, such as current and past CPATT students, research associates, and UW staff through a well-articulated “CPATT Database Survey”. \n \nData quality control and datasets format consistency of existing CPATT data were of a major concern, addressed by this research. This concern is addressed by providing numerous standard datasets formats and quality control checks; for dataset utilization to be more feasible and valuable for future researchers.
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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 | 0.057 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".