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Record W4248631497 · doi:10.1002/9781119263081.ch1

Formation Kinematics

2018· other· en· W4248631497 on OpenAlexaff
Hugh H. T. Liu, Zhu Bo

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsQuaternionEuler anglesKinematicsRotation (mathematics)Euler's rotation theoremMotion (physics)Euler's formulaUnit vectorRotation matrixMathematicsRigid body dynamicsRotation around a fixed axisRigid bodyClassical mechanicsMathematical analysisPhysicsGeometry

Abstract

fetched live from OpenAlex

This chapter introduces the notation to be used in the book, as well as the subject of vectorial kinematics, which is frequently used to derive equations of motion. The motion of an individual vehicle, is a six degree-of-freedom movement in space with respect to time. The physical description of motion of a mass point requires an origin to construct a vector. Translational motion between frames can be treated in the same way as Galilean transformation. Therefore, a distinctive description of translational or rotational dynamic behaviour is often developed through vectorial kinematics and dynamics. The chapter looks at the rotation matrix corresponding to consecutive rotations, represented by either their Euler axis/angle variables, or the Euler parameters. The Euler parameter is considered as a unit quaternion. Therefore the Euler parameter is considered as a unit quaternion.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.041
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0410.024

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 head0.006
GPT teacher head0.185
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes1
Has abstractyes

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