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Record W3135023813 · doi:10.7202/1075563ar

Phenotypic and genotypic characterization of tomato genotypes for resistance to root-knot nematode, Meloidogyne incognita

2020· article· en· W3135023813 on OpenAlexvenueno aff
Anupam Anupam, Narpinderjeet Kaur Dhillon, Sukhjeet Kaur, S. K. Jindal, Harwinder Singh Buttar

Bibliographic record

VenuePhytoprotection · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
Fundersnot available
KeywordsMeloidogyne incognitaBiologyRoot-knot nematodeTerra incognitaNematodeGenotypeNematologyMeloidogyne arenariaResistance (ecology)BotanyHorticultureAgronomyGeneGeneticsEcology

Abstract

fetched live from OpenAlex

Root-knot nematode is a major constraint to tomato cultivation in open and protected structures. Resistance sources need to be continuously identified for combating pathogens affecting the yield. In the present studies, forty-seven genotypes of tomato were evaluated phenotypically along with their genotypic characterization. On the basis of their phenotypic reaction, the genotypes were grouped into four categories viz.: resistant, moderately resistant, susceptible and highly susceptible. Of these genotypes, only five were found to be resistant while forty-two were rated from moderately resistant to highly susceptible. Multiplication of Meloidogyne incognita was greatly reduced (Rf < 1) in resistant genotypes as compared to susceptible genotypes. Root galling index was also very low in resistant genotypes. Using molecular markers, the presence of the Mi-1.2 resistance gene was also confirmed in five resistant genotypes (L-0272, NR-14, L-097, L-0275 and PNR-7). These resistant sources could become a source of germplasm in breeding programs for the development of resistant cultivars.

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 score0.688
Threshold uncertainty score0.253

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 head0.021
GPT teacher head0.211
Teacher spread0.189 · 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
Published2020
Admission routes1
Has abstractyes

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