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Record W3209346882 · doi:10.7202/1082505ar

Induction of resistance in tomato against buckeye rot (Phytophthora nicotianae var. parasitica)

2021· article· en· W3209346882 on OpenAlexvenueno aff
Adikshita Sharma, B. P. Shridhar, Amit Sharma, Monica Sharma

Bibliographic record

VenuePhytoprotection · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsnot available
Fundersnot available
KeywordsPhytophthora nicotianaeInoculationBiologyHorticulturePathogenPhytophthoraVeterinary medicineBotanyMicrobiologyMedicine

Abstract

fetched live from OpenAlex

Buckeye rot disease of tomato caused by Phytophthora nicotianae var. parasitica is the most destructive disease for reducing tomato yields especially in those regions where fruiting coincides with rainy season. In the present study, the pathogen was characterized by sequencing the DNA region coding for internal transcribed spacer (ITS) region and sequence was deposited in NCBI with accession no. MF398189. The phylogenetic analysis using the Maximum Composite Likelihood (MCL) approach revealed that the isolated pathogen clustered together with P. nicotianae with high bootstrap value of 99%. Incubation period of 120 h was observed in pin-prick method of pathogen inoculation compared to 168 h in surface inoculation method. Further, the disease resistance induced by nine different elicitors of induced resistance against buckeye rot disease of tomato were studied under field conditions for two consecutive years 2016 and 2017. Minimum disease incidence of 9.57% and 7.93% was observed with foliar spray of ß-aminobutyric acid (2 mM) for 2016 and 2017, respectively. It was followed by potassium chloride (100 mM) with disease incidence of 11.32% and 8.85% for year 2016 and 2017, respectively. Maximum fruit yield of 7.02 kg and 8.12 kg was found in treatment with ß-aminobutyric acid as compared to 2.61 kg and 2.55 kg in control for year 2016 and 2017, respectively.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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