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Record W4399653828 · doi:10.23977/jeeem.2024.070201

Optimization of agricultural broken film cleaning device based on electrostatic adsorption principle

2024· article· en· W4399653828 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Electrotechnology Electrical Engineering and Management · 2024
Typearticle
Languageen
FieldEngineering
TopicIndustrial Vision Systems and Defect Detection
Canadian institutionsnot available
Fundersnot available
KeywordsAdsorptionMaterials scienceAgricultureNanotechnologyChemistryBiologyEcologyPhysical chemistry

Abstract

fetched live from OpenAlex

Mulch planting and cultivation technology, known as the "second revolution" of agricultural production, is widely used in the development of agricultural production and promotes the increase of farmers' income. However, due to the difficult degradation of agricultural film materials and improper treatment methods, a large number of agricultural film fragments remain in the deep soil, causing great pollution to the soil environment. In this paper, through the analysis and research of the pollution status of agricultural broken film and the application status of agricultural film recycling device, it is proposed that the principle of electrostatic adsorption is used to adsorb and remove the small particle broken film in the deep soil, and the microwave drying technology is used to reduce the adhesion between the broken film and soil, so as to achieve the optimization of the existing device. The experimental results show that the optimized cleaning device can effectively remove the residual broken film in the deep soil, effectively reduce the white pollution in the soil, and provide a reference for the treatment of broken film pollution.

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: none
Teacher disagreement score0.768
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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

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