Closed-Loop Aeroservoelastic Analysis Validation Method
Bibliographic record
Abstract
3Ringertz, U. T., “An Optimal Trajectory for a Minium Fuel Turn,” Journal of Aircraft, Vol. 37, No. 5, 2000, pp. 932–934. 4Wanvik, V., “GPS Based Flight Mission Evaluation,” M.S. Thesis 99-07, Dept. of Aeronautics, Royal Inst. of Technology, Stockholm, 1999. 5Strang, G., and Borre, K., Linear Algebra, Geodesy, and GSP, WellesleyCambridge, 1997, pp. 467–470. 6De Boor, C., “Package for Calculating with B-Splines,” SIAM Journal on Numerical Analysis, Vol. 14, No. 3, 1997, pp. 441–472. 7Parkinson, B. W., and Enge, P. K., “Differential GPS,” Global Positioning System: Theory and Applications, edited by P. Zarchan, Vol. 2, AIAA, Washington, DC, 1996, Chap. 1, p. 26. 8Ringertz, U. T., “Estimating an Aircraft Trajectory Based on Uncertain Radar Data,” Technical report, Dept. of Aeronautics, Royal Inst. of Technology, 2000. 9Piegl, L., and Tiller, W., The NURBS Book, 2nd ed., Springer, 1997, Chap. 9, pp. 364, 365. 10Ringertz, U. T., Numerical Optimization with Applications in Structural Analysis and Design, Dept. of Lightweight Structure, Royal Inst. of Technology, Stockholm, 2000, Chap. 2.1.2, p. 7. 11de Try, F., “Aircraft State Reconstruction Using Limited Sensor Data,” Licentiate Thesis, TRITA/AVE 2003-03, Dept. of Aeronautical and Vechile Engineering, Royal Inst. of Technology, 2003. 12Stevens, B. L., and Lewis, F. L., Aircraft Control and Simulation, Wiley, New York, 1992, Chap. 1, p. 37.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".