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Record W2986784928 · doi:10.3390/agronomy9110754

Effective Plant Ages for Screening for Field Resistance to Alternaria Leaf Spot (Caused by Alternaria spp.) under Natural Infection in Jerusalem artichoke (Helianthus tuberosus L.)

2019· article· en· W2986784928 on OpenAlexfundno aff
Wanalai Viriyasuthee, Suwita Saepaisan, Weerasak Saksirirat, Mark L. Gleason, Ruey Shyang Chen, S. Jogloy

Bibliographic record

VenueAgronomy · 2019
Typearticle
Languageen
FieldNursing
TopicMicrobial Metabolites in Food Biotechnology
Canadian institutionsnot available
FundersLeibniz-Institut für Pflanzengenetik und KulturpflanzenforschungLeibniz-GemeinschaftRoyal Golden Jubilee (RGJ) Ph.D. ProgrammeThailand Research FundAgriculture and Agri-Food CanadaKhon Kaen University
KeywordsAlternariaLeaf spotBiologyHelianthusJerusalem artichokeHorticultureRandomized block designPlant disease resistanceInoculationTransplantingBotanyVeterinary medicineSunflowerMedicineSowing

Abstract

fetched live from OpenAlex

Host plant resistance has proven to be effective for controlling Alternaria leaf spot on Jerusalem artichoke (JA), but efficient screening techniques have not been developed yet. The objective of this study is to estimate the relationship between disease resistance parameters of JA as a function of plant age. Six JA varieties and three plant ages at the time of inoculation (20, 40 and 60 days after transplanting) (DAT) are evaluated in a factorial experiment in randomized complete block design (RCBD) with four replications. Disease incidence (DI) and severity (DS) are estimated, from which area under the disease progress curve (AUDPC) was calculated. Disease parameters are positively and significantly correlated for plant ages of 40 and 60 DAT. Based on our results, screening of JA at 40 DAT for resistance to Alternaria leaf spot is recommended. Knowledge of the impact of plant age on resistance to key diseases can help breeders to accelerate breeding programs so superior genotypes can be identified before reproductive growth stages.

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 categoriesMeta-epidemiology (narrow)
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 score0.038
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.264
Teacher spread0.253 · 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.

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

Citations11
Published2019
Admission routes1
Has abstractyes

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