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Record W2966213891 · doi:10.5539/jas.v11n14p56

Droplet Spectrum Produced in Pumpkin Cultures Submitted to Different Forms of Spraying

2019· article· en· W2966213891 on OpenAlexvenueno aff
Edney Leandro da Vitória, Déborah Hoffmam Crause, Ismael Forte Freitas Júnior, Tamara Locatelli, Élcio das Graça Lacerda, Juliana M. Valle, Marcelo Barreto da Silva, Adriano A. Fernandes, Rafael Zucateli da Vitória, Francisco de A. Ferreira, Joyce Lopes dos Santos, Silvério de Paiva Freitas

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleSprayerDrop (telecommunication)AmplitudeDeposition (geology)Spray nozzleFactorial experimentChemistryMaterials scienceAnalytical Chemistry (journal)MathematicsPhysicsOpticsChromatographyAgronomyBiologyMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The uniformity and droplet size produced during the spraying as well the correct deposition of these in the target, contribute directly to the success of a pesticide application. The objective of the present study was to characterize the ejected spray in the aerial and terrestrial spraying of pumpkin crops, with the use of adjuvants in a liquid solution. The experiment was carried out in two commercial plantations, in an entirely randomized design, employing a 6 × 2 factorial scheme, with six forms of application and two liquid compositions. The droplet spectrum was assessed employing water-sensitive card imaging. Smaller drop sizes and relative amplitudes were produced by aerial applications. In turn, the largest droplet diameters and the lowest percentage of drops smaller than 100 μm were obtained when using air induction twin flat spray nozzles. The adjuvant did not interfere in the numerical and volumetric median diameters, the relative amplitude, or the volume rate of droplets smaller than 100 μm.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.158

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.211
Teacher spread0.201 · 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 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

Citations13
Published2019
Admission routes1
Has abstractyes

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