ADS-33 Evaluation of the International CH-47 Chinook
Bibliographic record
Abstract
The DAFCS (Digital Automatic Flight Control System) equipped CH‐47 Chinook has been successfully deployed for the US Army and numerous international customers. Several of those international customers contracted with Boeing for ADS‐33 evaluations of the handling qualities of the aircraft including both the predicted and assigned (i.e. the Mission Task Elements) handling qualities. This paper covers the tailoring of ADS‐33 for the Chinook helicopter, the conduct of ADS‐33 testing and the results of the ADS‐33 testing. The primary focus of this paper will be centered on the Canadian long‐range CH147F ADS‐33 evaluation, which was a complete evaluation against all sections of ADS‐33 and was flown in 2012 and 2013. Additional lessons from two other standard‐range international customer evaluations, flown in 2013 and 2014 are also included. All of the configurations featured the DAFCS OFP 3.2 control laws, or a derivative thereof, which was originally developed for the US Army long range configuration. The section on the tailoring of ADS‐33 describes the process by which Boeing tailored ADS‐33E‐PRF for the Chinook and includes recommendations for future releases of ADS‐33. The assessment section describes the test techniques used and conduct of the testing, along with a summary of the results from both the predicted and assigned sections of ADS‐33.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".