MétaCan
Menu
Back to cohort
Record W2737793198 · doi:10.4050/f-0073-2017-12152

Future Advanced Rotorcraft Drive System (FARDS) Full Scale Gearbox Demonstration

2017· article· en· W2737793198 on OpenAlexaff
Andrea Chavez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsScale (ratio)Automotive engineeringAerospace engineeringAeronauticsComputer scienceControl engineeringEnvironmental scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

The Future Advanced Rotorcraft Drive System (FARDS) program focused on improving the performance and affordability of current aircraft drive systems. During the course of the program eighteen enabling technologies were developed to achieve these objectives within an existing helicopter transmission system. The transmission system was designed to enhance the Bell 407 light commercial aircraft configuration which has a similar configuration to the OH-58D Kiowa Warrior. Full scale main rotor gearbox testing was completed in 2016, which demonstrated many of these enabling technologies to a Technology Readiness Level (TRL) of 6. The demonstration testing was designed to closely follow the civil certification process (similar to military qualification testing), and included gear tooth pattern development, gear tooth bending fatigue, endurance, and three loss-of-lubrication tests. This testing successfully demonstrated a significant improvement in drive system technologies. The developed technologies are now ready to transition to future rotorcraft, such as Future Vertical Lift.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.199
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicTribology and Lubrication EngineeringFrench-language works237,207