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Record W2992424437 · doi:10.52151/jae2006434.1230

Peristaltic Pumping System for Metering Aqueous Fertilizer

2024· article· en· W2992424437 on OpenAlexaff
Amrita Dey, Indra Mani, J. S. Panwar

Bibliographic record

VenueJournal of Agricultural Engineering (India) · 2024
Typearticle
Languageen
FieldEngineering
TopicFreezing and Crystallization Processes
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMetering modePeristalsisPeristaltic pumpAqueous solutionFertilizerEnvironmental scienceChemistryEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

In dryland areas, an aqueous fertilizer seed drill helps in proper germination of seed and good establishment of crops by providing soil moisture during sowing time. Until now a suitable pumping unit to meter and disperse aqueous fertilizer is not available. The objective of the present work is to optimize the design values of the pump variables for different discharge rates suitable for different dryland winter crops. A prototype pump was developed and evaluated with two levels of reel diameter (0.30 and 0.16 m); four levels of roller spacing (15.7,8.4 ; 6.29, 11.8 cm); three levels of liquid head (0.51, 0.46, and 0.41 m); three levels of rpm (50, 100, and ISO); and three flexible tube diameters (12.7,9.6, and 6.4 mm). Discharge data was collected from the flexible tubes mounted on the rollers of the pump at a particular rpm and for the given levels of other pump variables. At ISO rpm, an individual tube of 12.7 mm diameter gave highest discharge of441 I/h. For 0.1 m variation of head, discharge varied by a maximum of50 1/ h. An increase of 3.9 cm roller spacing affected the discharge by a maximum of 160 I/h at 150 rpm. The developed pumping system with nine rows is capable of delivering a discharge range of212 I/h to 396911h, which satisfies the water requirement of many dryland winter crops.

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 score0.006

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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
Published2024
Admission routes1
Has abstractyes

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