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Record W4377212020 · doi:10.23977/acss.2023.070315

Experimental Study on the Liquid Medicine Recovery System of Vineyard Spray

2023· article· en· W4377212020 on OpenAlexvenueno aff
Jie Li, Guang Chen, Min Wang, Changkuan Lu

Bibliographic record

VenueAdvances in Computer Signals and Systems · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsnot available
FundersModern Agricultural Technology Industry System of Shandong province
KeywordsSprayerVineyardAgricultural engineeringAgricultureEnvironmental scienceEngineeringHorticultureGeographyBiology

Abstract

fetched live from OpenAlex

Grape is an important economic crop in Hebei, and its plant protection operation also accounts for an important proportion in production. Grapes need to be sprayed many times to kill pests in the whole growth cycle. Traditional farmers use electric backpack spray to spray pesticides in plant protection operations. This is inefficient, heavy workload, and inefficient use of pesticides. Many redundant agricultural chemicals sprayed into the soil will also lead to environmental pollution. In order to solve the related problems, a kind of liquid medicine recovery spray system was studied on the vineyard planting mode and canopy growth information design. The axial flow fan was used as the atomization device, and the liquid medicine recovery device was added to cooperate with the whole machine, so as to realize the automatic air delivery spray operation. Such spray system can effectively realize automatic spray operation, reduce the labor of farmers, recycle the redundant liquid medicine, and significantly improve the liquid medicine use efficiency of spray operation.

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 score0.010

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.043
GPT teacher head0.260
Teacher spread0.217 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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