MétaCan
Menu
Back to cohort
Record W2077130627 · doi:10.1504/ijvas.2014.057838

Application of a dynamic pressure-sinkage relationship for lightweight mobile robots

2013· article· en· W2077130627 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Vehicle Autonomous Systems · 2013
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEngineeringDynamic pressureGeotechnical engineeringSlip (aerodynamics)TestbedSuspension (topology)Structural engineeringMathematicsAerospace engineering

Abstract

fetched live from OpenAlex

This paper investigates a dynamic pressure-sinkage relationship which can be used in a wheel-soil model to better capture periodic variations observed in sinkage, drawbar pull and normal force as a rigid wheel interacts with loose sandy soil. The dynamic wheel-soil model can be used for wheels with or without grousers. Several case studies are presented to demonstrate the usefulness and applicability of this dynamic pressure-sinkage relationship. Hill climbing experiments were carried out using a smooth-wheel micro rover with a fixed suspension to confirm operational regions of the dynamic pressure-sinkage relationship. Single wheel testbed experiments were carried out to determine how well the model can predict changes in the number and length of the grousers on the wheel. It was concluded that dynamic pressure-sinkage relationship can predict the observed oscillations in the sinkage, drawbar pull and normal force with a single tuning case as the slip ratio and the configuration of the wheel changes.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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