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Record W4391763345 · doi:10.53555/sfs.v10i1s.2296

Effect Of Nitrogen And Potassium On Pomegranate Cv. Bhagwa Under Red And Lateritic Zone Of West Bengal

2023· article· en· W4391763345 on OpenAlexvenueno aff
Srikanta Chell, Samarpita Roy, Rajdeep Mohanta, Tanmoy Mondal, Sanghamitra Layek, Kamal Kumar Mandal

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldNursing
TopicPomegranate: compositions and health benefits
Canadian institutionsnot available
Fundersnot available
KeywordsWest bengalBENGALNitrogenPotassiumHorticultureBiologyMathematicsBotanyChemistryGeologyOceanographyEconomicsSocioeconomics

Abstract

fetched live from OpenAlex

The experiment was conducted with five levels of nitrogen and four levels of potassium per plant with twenty treatment combinations were applied to three years old pomegranate cv. Bhagwa trees at the Regional Research Sub-Station, Sekampur, Birbhum of Bidhan Chandra Krishi Viswavidyalaya, West Bengal. The highest yield was observed with N60 (4.103 kg/plant). In potassium application the yield /plant varied between 2.96 kg/plant (K0) to 3.664 kg/plant (K40). The combined application of N80K40 resulted in a 28.07 % (212.60g) increase in fruit weight compared to the control treatment (166.00g). The highest number of fruits/plants was recorded in treatment N60 (20.438) and lowest in control (13.688). Significantly maximum number of fruits /plants was noted in K40 (18.550). The highest increase in aril weight and juice weight could be observed with the application of N60K60 treatment. Quality analysis of the fruits showed maximum TSS with the 60 g/plant k. It is concluded that the combined application of N@60g and K@40 g per plant is beneficial in improving the yield and quality of three years old pomegranate plants under the red lateritic zone of West Bengal.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.092
GPT teacher head0.312
Teacher spread0.220 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Survey in Fisheries SciencesSame topicPomegranate: compositions and health benefitsFrench-language works237,207