Development of a Pavement Management and Prioritization Framework for Three Active Municipal Landfills
Bibliographic record
Abstract
In early 2011, a study was carried out to assess the condition of the haul road networks within three major active landfills in a large Canadian city. The purpose of this study was to identify and document the road segments within each landfill, determine the condition and structure of each road, and develop maintenance, rehabilitation or reconstruction (M, R & R) strategies based on the collected data. The pavement structures within each landfill consisted of flexible pavements (asphalt concrete), gravel pavements and dirt roads. The Route ID is an identifier used to develop a comprehensive pavement management database and to document all road segments within each landfill. The roads within each landfill were then sectioned using digital aerial images and site visits. Pavement attribute data was then collected for each unique identifier. To assess the condition of the pavements, condition surveys and deflection testing using a Falling Weight Deflectometer (FWD) were performed on all road segments. To identify the pavement structure, Ground Penetrating Radar (GPR) surveys and borings were advanced along each road segment. The collected data was then analyzed and used to develop M, R & R strategies for each roadway section. A prioritization methodology was also developed based on traffic levels, pavement thickness and structural condition. The pavement management methodology and prioritization strategy developed as a part of this study can be used by landfill operators to effectively manage their haul road networks and improve efficiency and operation. For the covering abstract of this conference see ITRD record number 201211RT334E.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".