MétaCan
Menu
Back to cohort
Record W7118170274 · doi:10.23977/jeis.2025.100219

Design of an Integrated Machine for Washing and Packaging Leafy Vegetables

2025· article· W7118170274 on OpenAlexvenueno aff
Gui Lu, Yizhe Zhang, Manhua Lu, Liude Liang, Jiaqiang Shi, Jia Yao

Bibliographic record

VenueJournal of Electronics and Information Science · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicSmart Agriculture and AI
Canadian institutionsnot available
Fundersnot available
KeywordsLeafy vegetablesAutomationLeafyModular designWorkflowFood processingProduct (mathematics)Work (physics)

Abstract

fetched live from OpenAlex

To address current issues in the leafy vegetable processing industry such as high reliance on manual labor, low efficiency, and difficulties in maintaining hygiene standards, this study designed and developed an intelligent integrated leafy vegetable cleansing and packaging machine. This equipment incorporates functions including root removal, yellow leaf elimination, washing, dewatering, and packaging. Based on a modular design concept, it achieves fully automated operation throughout the leafy vegetable processing workflow through the integration of precision mechanical structures and an intelligent control system. The equipment is adaptable to various common leafy vegetable varieties, demonstrating good versatility. It is constructed using food-grade stainless steel and silicone materials, meeting food safety and hygiene standards. The machine is simple and convenient to operate with low maintenance costs. This research outcome not only addresses the industry pain point of low automation in leafy vegetable processing, significantly reducing labor costs and work intensity, but also provides the agricultural product processing industry with an efficient, environmentally friendly, and safe intelligent solution. It holds significant practical value and broad application prospects.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.006
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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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 designOther design
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Electronics and Information ScienceSame topicSmart Agriculture and AIFrench-language works237,207