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Record W2912565299 · doi:10.5539/jas.v11n3p180

Effect of Calcium Hypochlorite in Drip Irrigation System After Application of Red Potassium Chloride

2019· article· en· W2912565299 on OpenAlexvenueno aff
Floriano Luiz Suszek, Márcio Antônio Vilas Boas, S. C. Sampaio

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsnot available
Fundersnot available
KeywordsFertigationFertilizerCloggingDrip irrigationIrrigationEnvironmental sciencePotashEnvironmental engineeringAgronomyGeography

Abstract

fetched live from OpenAlex

The use of water in agriculture should be monitored in order to be more efficient, with irrigation being the largest consumer of water in rural areas. Small farmers who are part of family farming have few resources to develop profitable activities and aiming their well-being, have created family irrigation kits which is a small drip irrigation system. The systems durability depends of some factors like clogging risk of drippers, which is often caused by the quality of the water used or the application of fertilizers to the irrigation water. This research aimed to analyze a family irrigation system with application of red potassium chloride, followed by a chemical agent to clear drippers (calcium hypochlorite) with water application before and after the fertilizer and chemical products (four treatments). At the after, the evaluations were analyzed with statistical quality control charts where the problem was detected in the final of application of the fertilizer, and the process capacity was analyzed showing that it was capable and also was analyzed the degree of system clogging in relation to the obstruction with the fertilizer and clearing with the chemical agent, which resulted in the recovery of the irrigation system (initial flow and distribution uniformity same the final) with recommendations for the use of fertilizers by drip irrigation like open the end lines after the fertigation and dissolve the fertilizer fully before application.

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)0.002
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 score0.800
Threshold uncertainty score0.132

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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