Asset Management of Gravel Airstrips in the Yukon Canada
Bibliographic record
Abstract
The Yukon Department of Highways and Public Works operates a series of gravel airstrips in isolated parts of the Yukon. In addition to the primary function of providing services to general aviation, these isolated airstrips support medical emergency evacuations and act as temporary bases for forest fire fighting aircraft and equipment. The Department has well-established bridge inspection/management, pavement and bituminous surface treatment management systems for its highway network and a pavement management system for its major airports. Lacking, however, was an inspection and rating system for its gravel airstrips. This paper describes the establishment of a rating system for these low volume gravel airstrips. It identifies the distresses that should be monitored during an annual inspection. A number of composite indices were developed similar to the Pavement Condition Index (PCI) used for pavement management, for use with the gravel airstrips. Due to the variation of gravel surfaces depending on weather and the last gravel blading, a general condition index based on the overall visual observation of the airstrip was judged to be more effective than composite indices based on individual distresses. The major feature of the management system for these airstrips is the identification of maintenance items required for the operation and preservation of the airstrip and the identification of the time frame required for major capital investments (gravel resurfacing).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".