Classification of critical spares for aircraft maintenance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Maintenance planning is a major aspect for the aircraft manufacturers and airlines. Not having adequate spare parts in the inventory for the scheduled maintenance could result in costly flight cancellation with a negative impact on airline performance. An excess of spare parts inventory, on the other hand, leads to a high holding cost. Since airline industries involve with a large number of parts and some of them are quite expensive, it is important to classify critical parts needed to be kept in the inventory with minimal system costs. This study focuses on classifying spare parts into three groups by using traditional, analytic hierarchy process (AHP), and data envelopment analysis (DEA) methods based on factors associated with spare parts: unit price, usage rate, lead time, and reliability. Results show that it is advantageous to use DEA method to classify the inventory.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it