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Record W4400201923 · doi:10.23977/jemm.2024.090120

Structural Design and Innovation of Gordon Euryale Shell from the Perspective of Agricultural Mechanization

2024· article· en· W4400201923 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPolysaccharides Composition and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMechanizationAgricultureIndustrial organizationAgricultural machineryDiversification (marketing strategy)EngineeringProduction (economics)Agricultural engineeringManufacturing engineeringBusinessEconomicsMarketing

Abstract

fetched live from OpenAlex

With the acceleration of agricultural mechanization, the technical innovation of Gordon euryale shell machine has become the key to improve the efficiency of Gordon euryale industry. This paper comprehensively analyzes the development trend of agricultural mechanization, especially the driving effect on the demand of Gordon seed shell remover. As an important tool connecting agricultural production and market, the structural design and innovation direction of Gordon euryale seed deshell machine directly affect the development and upgrading of Gordon euryale seed industry. This paper summarizes the current state of agricultural mechanization, analyzes the unique decapping requirements of the industry and the relationship between the two, then details the key points of design innovation, including innovation opportunities brought by technological progress and function expansion and diversification strategies. It also discusses the potential impact of the machine on agricultural production mode and industrial chain, and points out the development trends and challenges.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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Same venueJournal of Engineering Mechanics and MachinerySame topicPolysaccharides Composition and ApplicationsFrench-language works237,207