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Record W4214576086 · doi:10.3390/ma15041568

Effect of Stone Impacts on Various Ground Engaging Tools (Flexible/Stiff Tines and Coulter): Part I

2022· article· en· W4214576086 on OpenAlexaff
A. Lisowski, Adam Świętochowski, Magdalena Dąbrowska, J. Klonowski, T. Nowakowski, J. Chlebowski, Przemysław Tryskuć, Tomasz Parys, Samuel Ferré, Martin Roberge

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsUniversity of SaskatchewanCNH Industrial (Canada)
Fundersnot available
KeywordsTineVibrationGeologyEngineeringMechanical engineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

Analysis of the state of knowledge showed a gap in the description of tool–stone feedback. Therefore, the aim of this study was to investigate tool–stone interactions. Spherical-like silicate stones were hit by stiff and flexible tines with a duckfoot or a coulter. The tools worked with various parameters in the depth range of 0.05–0.10 m and a speed of 0.83–2.22 m·s–1. The characteristics of stone movement were specific to the type of tool and were described by the Numerical Stone Movement Scale developed for the purpose of the research. After the impact with the stiff tine, the stones were thrown the greatest distance of 0.26–1.08 m, and these distances were strongly dependent on the working speed and slightly dependent on the working depth. Large vibrations of the flexible tine and the location of the contact point of the tine in relation to the centre of the stone thickness contributed to the random behaviour of stones that were slightly moved, rotated or displaced. The specific work required to remove the stone reflected the distance travelled by the stone as well as the specific force which largely contributed to increasing the differences in this work between both tines.

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)0.001
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 score0.028
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2022
Admission routes1
Has abstractyes

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