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Record W2976835688 · doi:10.5539/jas.v11n17p187

Deposition, Endo-drift and Exo-drift in the Pulverization in Coffee With Different Equipment

2019· article· en· W2976835688 on OpenAlexvenueno aff
Tamara Locatelli, Silvério de Paiva Freitas, Ismael Forte Freitas Júnior, Edney Leandro da Vitória, Sávio da Silva Berilli, Sílvio de Jesus Freitas, André Cayô Cavalcanti, Tallita Pedroni Locatelli, Juliana Valle, Giacomina Possatti Lepaus, Déborah Hoffmam Crause

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsSprayerKnapsack problemNozzleDeposition (geology)Randomized block designMathematicsAgricultural engineeringEnvironmental scienceEngineeringAlgorithmMechanical engineeringGeologyStatistics

Abstract

fetched live from OpenAlex

The objective was to evaluate the equipment efficiency in reducing drift and increasing the spray deposition. The experiment was conducted of the conilon coffee plantation, located on the experimental area of the Federal Institute of Espirito Santo, Itapina, Brazil. The experiment was a randomized complete block design with four replications. The treatments consisted: a knapsack sprayer with electrostatic assistance, an electric knapsack sprayer, a knapsack sprayer with a spray shield, and a knapsack sprayer without a spray shield. All sprayers were equipped with a single spray nozzle. Spray deposition was evaluated on wee leaves using a food colourant as a tracer. The knapsack sprayer with electrostatic assistance was the most efficient equipment, providing lower values of drift, and the greatest deposition on the weeds. It is recommended to use the electrostatic sprayer, as it showed greater efficiency in the application of the product on the target, using smaller volume

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 score0.002
Threshold uncertainty score0.004

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.183
Teacher spread0.174 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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