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Record W2100884632 · doi:10.5539/mas.v3n9p95

The Performance Study of Hybrid-driving Differential Gear Trains

2009· article· en· W2100884632 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueModern Applied Science · 2009
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsDifferential (mechanical device)Computer scienceTrainGear trainTest benchEncoderControl theory (sociology)Mechanism (biology)Controller (irrigation)Variable (mathematics)Automotive engineeringControl engineeringEngineeringControl (management)MathematicsArtificial intelligencePhysicsEmbedded system

Abstract

fetched live from OpenAlex

This article in view of the differential gear trains theory characteristic, carried on the analysis to the mechanism performance, and obtained the best design scheme of the Hybrid-driving two degree of freedom differential gear trains. Adopted programmable logic controller (short for PLC) component and frequency converter to control the constant speed motor and the variable speed motor, developed a laboratory bench of Hybrid-driving two degree of freedom differential gear trains and utilized a encoder to gather the experimental data under this condition. This laboratory bench will be used as a research platform for basic theory of textile machinery and providing the rationale to optimize its variable speed mechanism.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.340

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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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