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Record W2086343584 · doi:10.2135/cropsci2000.402444x

Spray Chamber Evaluation of Air‐Assisted Spraying on Broccoli

2000· article· en· W2086343584 on OpenAlexaff
B. Panneton, H. Philion, R. Thériault, Mohamed Khelifi

Bibliographic record

VenueCrop Science · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Surface Properties and Treatments
Canadian institutionsUniversité LavalAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAirspeedAirflowCanopyNozzleJet (fluid)Environmental scienceHorticultureVolume (thermodynamics)SprayerAerial applicationAtmospheric sciencesMaterials scienceMeteorologyBotanyAgronomyBiologyMechanicsPhysicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Conventional over‐the‐row sprayers achieve very little deposit on leaves near the ground and on the underside surfaces of leaves throughout the canopy. Air assistance has the potential to improve deposition of droplets on these leaf surfaces. To gain some insight on the effect of air assistance, the effects of airspeed, airflow rate, and air jet orientation were isolated. The study was carried out in a spray chamber with a standard spray boom over micro‐plots of greenhouse grown broccoli ( Brassica oleracea var. botrytis L.) plants. Air was delivered slightly behind the nozzles from a variable width slot producing a uniform two‐dimensional air jet. The orientation of the air jet with respect to the vertical could be adjusted from −10 to 40°. The ranges of the independent variables were airspeed, 0 to 36 m s −1 ; airflow rate, 0 to 1.3 m 3 s −1 m −1 , and air jet angle, −10.2 to 40.2°. Two sets of flat fan nozzles (Volume Median Diameter = 230 and 400 μm, both delivering 250 L ha −1 at 6 km h −1 ) were used to carry out two full sets of experiments. Results showed that airspeed had the larger impact on leaf coverage. Higher airspeeds (>25 m s −1 ) and airflow coupled with finer sprays increased the coverage of the underside of the leaves at all levels within the canopy and of the top side of the leaves in the lower third of the canopy. However, lower airspeeds (<20 m s −1 ) are desirable for a better coverage of the upper side of the leaves in the higher two‐thirds of the canopy. In all cases, angling the air jet forward at 20 to 25° is recommended.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.062
GPT teacher head0.272
Teacher spread0.210 · 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.

Study designObservational
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
Published2000
Admission routes1
Has abstractyes

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