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Record W125205238 · doi:10.22260/isarc2013/0083

Determining of Drivetrain System Skid Steer 6x6 Wheeled Robot Load

2013· article· en· W125205238 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueProceedings of the ... ISARC · 2013
Typearticle
Languageen
FieldEngineering
TopicEngine and Fuel Emissions
Canadian institutionsnot available
Fundersnot available
KeywordsSkid (aerodynamics)DrivetrainAutomotive engineeringEngineeringRobotMobile robotObstacle avoidanceHydraulic machineryTorqueComputer scienceMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Determining of Drivetrain System Skid Steer 6x6 Wheeled Robot Load M. J. Lopatka, T. Muszynski Pages 763-773 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: This paper presents the results of 3 tons skid steer 6x6 wheel robot drive system field tests. The aim of the investigation was verification of assumed data for drivetrain diesain and forces identification in the drivetrain system during maneuvering and crossing obstacles. Robot has hydraulics drive system with motors in the wheels and hydraulic suspension system. The neded drive torque on wheels was determined by pressure measurnig on motors. Keywords: Skid-steer vehicle, UGV, Robot, Obstacle, Turning and roling resistance, 6x6 vehicle, Hydrostatic drive DOI: https://doi.org/10.22260/ISARC2013/0083 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.481

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 head0.005
GPT teacher head0.166
Teacher spread0.161 · 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