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Record W4200101342 · doi:10.35495/ajab.2020.01.039

Induction of resistance in onion against purple leaf blotch disease through chemicals

2021· article· en· W4200101342 on OpenAlexfundno aff
Mohammad Younas, Muhammad Atiq, Ahmed Nasir, Wasim Abbas, Muhammad Rizwan Bashir, Salman Ahmad, Muhammad Ullah, Waqas Ashraf Bhatti, Nadia Liaqat, Irfan Ahmad

Bibliographic record

VenueAsian Journal of Agriculture and Biology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
FundersUniversity of Agriculture, FaisalabadAlberta Agricultural Research Institute
KeywordsBiologyPlant disease resistanceAgronomyCultivarResistance (ecology)Insecticide resistanceHorticultureToxicologyGene

Abstract

fetched live from OpenAlex

Onion is one of the world's most important vegetable crop cultivated in Pakistan and plays a significant role in human diet. Numerous diseases attack on onion crop, but purple leaf blotch is the most important one, because it causes 80 to 90% of onion yield loss all over the world. In current experiment twenty-three fungicides at three concentrations (0.5, 1, and 1.5 g/L) were evaluated against Alternaria porri causing purple blotch under Randomized Complete Block Design (RCBD) on susceptible variety of onion (Pink Panther). Among all fungicides, chlorostrobin expressed prominent results causing 62.05% reduction in disease severity, followed by Nanok (61.55), Shincar (54.86), Cabrio Top (53.33), Thril (50.00), Jalwa (48.11), Success (45.00), Alliette (41.61), Rally (39.83), Copper oxychloride (36.66), Score (33.05), Topas (29.88), Melodydue (13.27), Dithane M (11.66), Sulphax (6.55), Ridomil Gold (3.38) % respectively as compared to control. Similar results were observed in case of interaction b/w treatments and their concentrations. Results of current study are helpful for farmers, scientist, and researchers for timely management of purple leaf blotch disease of onion.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.010
GPT teacher head0.237
Teacher spread0.226 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAsian Journal of Agriculture and BiologySame topicPlant Pathogens and Fungal DiseasesFrench-language works237,207