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Record W4212806497 · doi:10.3390/ma15041351

Kinetics and Dynamics of the Stiff and Flexible Tines with the Duckfoot and the Coulter after Impact with Stones Embedded in Compacted Soil. Part II

2022· article· en· W4212806497 on OpenAlexaff
A. Lisowski, Adam Świętochowski, Magdalena Dąbrowska, J. Klonowski, T. Nowakowski, J. Chlebowski, Samuel Ferré, Martin Roberge

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsUniversity of SaskatchewanCNH Industrial (Canada)
Fundersnot available
KeywordsTineCoulter counterEnhanced Data Rates for GSM EvolutionMaterials scienceGeologyGeotechnical engineeringComposite materialEngineeringStructural engineeringTelecommunications

Abstract

fetched live from OpenAlex

The kinetics and dynamics of the stiff and flexible tines with the duckfoot and the coulter after impact with stones embedded in compacted soil were examined. The beak of the duckfoot was positioned in the axis of the row of stones embedded in the soil at the depth of stones thickness. The coulter covered the stone or impact the edge of the stone halfway along its length. The tools worked at a speed of 0.83–2.22 m·s−1 and a working depth of 0.05–0.10 m. The results of specific parameters were compared to the response of the tools to loads in soil without stones. For both soil conditions, the kinetics of the flexible tine was 24 times more reactive, and the dynamic loads were two times lower than for the stiff tine. The responses of both tines were suppressed along with the working depth because of the more favorable place of impact of the duckfoot beak with the stone. Along with the working speed, for a stiff tine, the specific accelerations decreased significantly, by ten times, and the specific forces increased slightly, by 1.6 times. Among the two systems of setting the coulter, the impact of the cutting edge of the coulter with the stone in the middle of its length was more unfavorable than the work of the coulter covering the stone.

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 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 score0.261
Threshold uncertainty score0.252

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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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 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
Published2022
Admission routes1
Has abstractyes

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