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Record W4403848542 · doi:10.5539/jsd.v17n6p118

Evaluation of the Structure of Dry Soil, Subjected to Dynamic Load in Different Managements

2024· article· en· W4403848542 on OpenAlexvenueno aff
Emerson Trogello, Elton Fialho dos Reis, Paulo Viana, Rone E. Barbosa

Bibliographic record

VenueJournal of Sustainable Development · 2024
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultural engineeringBusinessEngineering

Abstract

fetched live from OpenAlex

The structural evaluation of agricultural soil is essential for the performance and quality of production. Soils suffer surface consolidation when subjected to agricultural machinery traffic and alter their physical characteristics according to the management to which it is subjected. Therefore, this work aimed to evaluate the physical indices and surface consolidation of a red oxisol subjected to different loads and types of management. Undeformed soil samples were collected from three areas with different management systems: no-tillage, crop-livestock--forest integrated system (CLFI), and pasture area. Traffic simulations were conducted with application of dynamic loads, using 0 pass, 1 pass, and 2 passes (0, 108, and 216 kpa), evaluating the physical indices of the soil's natural specific weight (yn), dry apparent specific weight (yd), void index (n), and porosity (e). Subsequently, the samples were subjected to uniaxial compression tests using loads (108, 216, 432, 864, and 1500 Kpa) to verify the surface consolidation. The experiment was conducted in a completely randomized design, with a 3x3x5 factorial scheme in sub-subdivided plots. Thus, we verified that the no-tillage management system has a more porous soil due to its characteristics, presenting lower resistance and being better for planting. CLFI was characterized as a management that allowed the soil to be more resistant to the application of loads and, therefore, less porous. Pasture has an intermediate behavior.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sustainable DevelopmentSame topicSoil Mechanics and Vehicle DynamicsFrench-language works237,207