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Record W2168824736 · doi:10.1080/07060661.2011.558523

Virulence, distribution and diversity of <i>Rhizoctonia solani</i> from sugar beet in Idaho and Oregon

2011· article· en· W2168824736 on OpenAlexvenueno aff
C.A. Strausbaugh, Imad A. Eujayl, Leonard W. Panella, Linda E. Hanson

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsRhizoctoniaSugar beetBiologyRhizoctonia solaniRoot rotVirulenceAgronomyHorticultureVeterinary medicineMedicine

Abstract

fetched live from OpenAlex

Abstract Rhizoctonia root rot causes serious losses on sugar beet worldwide. In order to help explain why Rhizoctonia root rot management practices have not performed well in some areas of the Intermountain West (IMW), a survey was conducted. In the IMW from 2004 to 2006, 94 Rhizoctonia solani field isolates were collected from sugar beet roots. These field isolates were compared with 19 reference strains and 46 accessions from GenBank for genetic diversity based on sequencing of the ITS-5.8S rDNA region. Greenhouse pathogenicity tests on sugar beet and silage corn were conducted and plant damage was assessed using a randomized complete block design with at least four replications. The majority of the isolates had sequence identity with the AG-2-2 IIIB (47%) or AG-4 subgroups (44%). Most of the AG-2-2 isolates (87%) were associated with fields in the western portion of the production area, while 71% of the AG-4 isolates came from the eastern portion of the production area. Isolates from AG-2-2 IIIB were frequently more virulent on sugar beet and sequence of the ITS-5.8s region required cloning because of genetic diversity within isolates. Seven (all AG-2-2 IIIB) of 18 isolates tested could attack both sugar beet and corn, while two of the seven virulent isolates caused less root rot on corn. To reduce Rhizoctonia root rot on sugar beet and corn, crop rotations and the isolates utilized for selecting host resistance could be given further consideration.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.869

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 head0.017
GPT teacher head0.160
Teacher spread0.143 · 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 designObservational
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

Citations62
Published2011
Admission routes1
Has abstractyes

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