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Record W2626559168 · doi:10.6000/1927-5129.2017.13.50

Effect of Different Levels of Zinc on the Growth and Yield of Cotton (Gossypium hirsutum L) Crop

2017· article· en· W2626559168 on OpenAlexvenueno aff
A. H. Kaleri, Arshad Ali Kaleri, Shabana Memon, Abdul Latif Laghari, Saima Bano, Musarat Mallano, Majid Hussain Kaleri

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicResearch in Cotton Cultivation
Canadian institutionsnot available
Fundersnot available
KeywordsKharif cropRandomized block designZincCropField experimentYield (engineering)PopulationHorticultureAgronomyGossypiumBiologyAnimal scienceMathematicsChemistryMedicine

Abstract

fetched live from OpenAlex

An experiment was conducted to determine the effect of different levels of zinc on the yield and growth of cotton in the field of Agronomy section ARI, Tandojam during the Kharif Season 2014. Seeds of cotton were sown in rows 75 x 30 cm in row and plant spacing in soil with four replications in Randomized Complete Block Design. Six zinc levels i.e. untreated 0.0, 5.0, 7.5, 10.0, 12.5 and 15.0 kg ha-1 were evaluated. The results reveals that plant height, number of sympodia plant-1, number of productive bolls plant-1, fibre length, G.O.T (%) and seed cotton yield kg ha-1 affected significantly by the zinc levels, while plant population and number of monopodial branches were not affected. Application of zinc from 5.00 to 15.00 showed similar effect. However, control resulted different in taller plants (130.55 in), while application of 15.00 kg zn ha-1 produced maximum sympodia (16.35 plant-1) however productive bolls were more at 10.00 kg zn ha-1 (50.30 plant-1). The staple length was maximum (27.00 mm) at 7.5 kg zn ha-1, while G.O.T% was greater (38.28%) at 5.00 kg zn ha-1, whereas maximum seed cotton yield was recorded at 7.5 kg zn ha-1 (2556.70 kg ha-1). For the trait of seed cotton yield plant1, there was no any difference between applications of zinc sulphates 5.00 to 7.5kg ha1.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.068
GPT teacher head0.301
Teacher spread0.233 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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