Parametric Cost Prediction Models for Light General Aviation Aircraft in the Early Design Phase
Bibliographic record
Abstract
Among many methods used to predict the cost, parametric cost analysis is widely used.This technique employs parameters not directly correlated to the product cost, like quantity/quality characteristics, to predict the aircraft cost.The data used in this paper for prediction relates to aircraft still "in production" and "in service" and presents costestimation models for light general aviation aircraft whose maximum take-off weight (MTOW) is less than 2000 kg.The Aircraft are classified into two categories based on the landing gear configuration.Important design parameters that are mostly known or easily calculated at the beginning of the preliminary design phase and affect the aircraft design are considered.Multi-linear regression analysis based on the p-value (also known as p-value analysis) is applied to develop the cost-estimation models.These empirical models presented in the paper can predict the cost to an error accuracy of less than ± 5% for all categories, and in the majority of cases, the cost prediction error accuracy of less than 3%.In addition, the models offer the possibility of performing parametric studies to obtain the cost sensitivity to the key design parameters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".