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Record W2942628073 · doi:10.5539/jas.v11n6p528

Pesticide Application Quality With Alternative Use of Water Sensitive Paper

2019· article· en· W2942628073 on OpenAlexvenueno aff
João Paulo Tomasini Castoldi, Diandra Ganascini, Luciene Kazue Tokura, Eduardo Lange Sutil, Soni Willian Haupenthal, Bruna de Villa, Isaque de Souza Mendes, Alessandra Mayumi Tokura Alovisi, Jéssica Cristina Urbanski Laureth, Márcio Furlan Maggi

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsnot available
FundersFundação AraucáriaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsNozzleEnvironmental scienceWater qualityEnvironmental engineeringMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The water sensitive paper is commonly used in the uniformity and distribution ratings of the spray droplets pattern; however, this technology still has a high cost. In this way, the present work had as objective to evaluate the quality of deposition of spray drops in different types of papers sensitive to water. The test was carried out in a wind tunnel built inside the multi-sport gymnasium of the State University of West Paraná-campus of Cascavel-PR. A semi-automated nozzle transition prototype was used to perform the tests. The variables evaluated were number of diameters, dispersion, volumetric medium diameter (VMD), droplet density, cover and droplet volume. The treatments consisted of four different types of paper compared to the water sensitive paper (control), and two nozzles. Cardboard paper stood out among the others at the cost of approximately 1% of a water-sensitive paper slip. The water sensitive paper used in agriculture can be replaced by alternative papers more economically viable and easily found in the market.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.024
GPT teacher head0.226
Teacher spread0.202 · 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
Published2019
Admission routes1
Has abstractyes

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