Investigation Methologies and Design for Runway Pavement Rehabilitation at Churchill Falls Airport
Bibliographic record
Abstract
Churchill Falls Airport is located in Churchill Falls, Labrador and is owned and operated by Nalcor Energy Company. The case study presented in this paper discusses the investigation and design methodologies that were used for the rehabilitation of the runway pavement. It includes the innovative testing tools and designs that can be utilized to investigate and address challenging geotechnical and climatic conditions in remote areas in the North. The pavement at Churchill Falls airport was significantly distressed including numerous areas of frost heaving and extensive cracking. The field investigation included a detailed pavement distress inspection, test pit investigation and a geophysical survey using Ground Penetrating Radar (GPR). It was determined from previous investigation and our own limited geotechnical investigation that the bedrock at the airport site was undulating and very shallow at some locations. The frost heaving of the runway pavement was due to frost susceptible subgrade soils (glacial till) and shallow undulating bedrock trapping groundwater. In order to develop a suitable rehabilitation strategy to the severity of frost heaving it was necessary to obtain a detailed map of the depth to bedrock. This mapping and continuous profile was obtained by carrying out a GPR survey. The results from the field investigations, in particular the GPR survey were used to develop pavement rehabilitation design alternatives and life cycle cost analysis to identify the most economically feasible alternative. One of the design alternatives developed included installation of polystyrene insulation to minimize frost penetration into the glacial till soils. For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".