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Record W4285502005 · doi:10.1002/9781119719984.ch2

Mechanics of Vehicle–Terrain Interaction

2022· other· en· W4285502005 on OpenAlexaff
J.Y. Wong

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsTerrainFinite element methodSlip (aerodynamics)Aerospace engineeringEngineeringComputer scienceSimulationStructural engineeringGeography

Abstract

fetched live from OpenAlex

Applications of the Theory of Elasticity to Predicting Stress Distributions in the Terrain under Vehicular Loads Applications of the Theory of Plastic Equilibrium to the Mechanics of Vehicle-Terrain Interaction Empirically Based Models for Predicting Off-Road Vehicle Mobility Measurement and Characterization of Terrain Response Bekker-Wong Terrain Parameters A Simplified Physics-Based Model for the Performance of Tracked Vehicles An Advanced Physics-Based Model for the Performance of Vehicles with Flexible Tracks An Advanced Physics-Based Model for Performance of Vehicles with Long-Pitch Link Tracks Physics-Based Models for the Cross-Country Performance of Wheels (Tires) A Physics-Based Model for the Performance of Off-Road Wheeled Vehicle Performance Slip Sinkage Applications of Terramechanics to the Study of Mobility of Extraterrestrial Rovers and Their Running Gears Finite Element and Discrete Element Methods for the Study of Vehicle-Terrain Interaction References Problems

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.594
Threshold uncertainty score0.987

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)0.0140.000

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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