A Framework for Flight Resource Optimization: Safety, Technology, Wellbeing, and Infrastructure for Northern Geography
Bibliographic record
Abstract
Many communities in northern Canada rely on small aircraft for the transportation of people and crucial supplies (food, fuel, medicine, etc.). Unfortunately, these aircraft face significant safety risks and high operating costs, with operators often requiring government subsidization to cover costs, leaving minimal funding for operators to make upgrades to infrastructure or equipment. Emerging Advanced Air Mobility technologies could substantially and positively impact these aircraft-reliant northern communities; however, this is a complex socio-technical system-of-systems problem that requires consideration of metrics that address the social and technical aspects of providing aviation-based support. This paper proposes a framework for FROSTWING (Flight Resource Optimization: Safety, Technology, Well-being, and Infrastructure for Northern Geography) and presents preliminary outcomes demonstrating the ability of the framework to capture important system interdependencies. To the author’s knowledge, no similar northern-community aviation model currently exists. The use of well-being metrics also sets this model apart from most aviation models. In the future, FROSTWING could inform decisions on which technology investments would result in the highest value impact for these underserved communities while lowering aviation risk factors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".