Non-destructive Structural Asset Valuation of a Saskatchewan Rural Airfield Before and After Structural Upgrade
Bibliographic record
Abstract
The Saskatchewan Ministry of Highways and Infrastructure is responsible for maintaining several northern Saskatchewan airfields. The Meadow Lake Airfield provides year round air service as well as a fire fighting support base to northern communities. In 2006, several areas of the Meadow Lake Airfield received structural rehabilitation treatments. The objectives of the structural asset management survey were to evaluate the potential use of ground penetrating radar (GPR) to quantify in situ structural composition, to evaluate the use of integrated GPR and heavy weight deflection (HWD) measurements and derive conventional Transport Canada Pavement Load Ratings (PLR), to quantify a priori structural asset management values of the airfield pavement sections, and to allocate and distribute funds into necessary rehabilitation and preservation treatments. An additional objective was to explicitly quantify the structural value added from the rehabilitation and preservation treatments performed in 2006. Based on the structural asset management survey using non-destructive GPR and HWD measurements, it was found that the structural rehabilitation treatments improved the surface quality and the structural response of the Meadow Lake Airfield and reduced subsequent variability. In summary, the structural asset management GPR and HWD measurement approach to surveying airfield pavement before and after various rehabilitation treatments demonstrates a reliable and repeatable means to measure structural improvements without damaging the airfield asset with conventional PLR test methods.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".