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Record W1988122099 · doi:10.1094/phyto.2000.90.9.1011

Influences of Cropping Practices on <i>Verticillium dahliae</i> Populations in Commercial Processing Tomato Fields in Ontario

2000· article· en· W1988122099 on OpenAlexaboutno aff
Myrtle Harrington, Katherine F. Dobinson

Bibliographic record

VenuePhytopathology · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
FundersOhio State University
KeywordsVerticillium dahliaeBiologySubspeciesRestriction fragment length polymorphismVeterinary medicineDNA profilingBotanyAgronomyPolymerase chain reactionEcologyDNAGenetics

Abstract

fetched live from OpenAlex

ABSTRACT The abundance of Verticillium dahliae in the soil and the incidence of V. dahliae-infected plants were determined for 12 commercial processing tomato fields in Kent County, Ontario. Comparison of the data with those from a previous survey of fields in adjacent Essex County showed that soil inoculum levels and incidence of infection were generally lower in Kent County fields and that race 2 V. dahliae was not common in Kent County. From the two counties, 128 isolates were characterized by restriction fragment length polymorphism (RFLP) analysis, using the subspecies-specific repetitive DNA sequence E18. A subset of these isolates was also characterized by vegetative compatibility and DNA hybridization analysis with a second subspecies-specific DNA sequence. Isolates with E18 RFLP profiles highly similar to those of isolates previously collected from potato fields in North America were prevalent in Essex County tomato fields but not common in Kent County fields. The data are consistent with the hypothesis that the group I isolates were introduced into southwestern Ontario with potato and that the different cultural practices in Essex County and Kent County have contributed to the differences in the accumulation of these isolates in the two regions.

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 score0.149
Threshold uncertainty score0.863

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 head0.054
GPT teacher head0.313
Teacher spread0.259 · 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

Citations18
Published2000
Admission routes1
Has abstractyes

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