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Record W2046485662 · doi:10.1094/pd-89-1143

Development of a DNA Macroarray for Detection and Monitoring of Economically Important Apple Diseases

2005· article· en· W2046485662 on OpenAlexafffund
P. L. Sholberg, D. T. O’Gorman, K.E. Bedford, C. André Lévesque

Bibliographic record

VenuePlant Disease · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaNational Institutes of HealthWashington Tree Fruit Research Commission
KeywordsBiologyVenturia inaequalisPenicillium expansumApple scabRibosomal DNAFire blightBotanyErwiniaGeneticsGeneCultivarFungicidePhylogenetic tree

Abstract

fetched live from OpenAlex

Short DNA gene sequences (oligonucleotides) from the ribosomal spacer regions of bacterial and fungal pathogens were used to identify and monitor economically important apple diseases. The oligonucleotides or probes were attached to a nylon membrane by an amine modified linker arm and arranged in a precise pattern to form an array for detecting five pathogens corresponding to five apple diseases. Initially the specificity of the probes was determined by hybridizing pure cultures of the pathogens to the probes. The DNA array correctly identified Botrytis cinerea, Penicillium expansum, Podosphaera leucotricha, Venturia inaequalis, and Erwinia amylovora and eliminated closely related species. When the array was used to monitor V. inaequalis ascospores collected from spore traps located in orchards, it confirmed the presence of ascospores as predicted by the disease forecasting model. Preliminary tests to quantify P. leucotricha populations using grayscale values was effective to 20 conidia per leaf disk. The DNA array is a promising new detection system for accurate identification of several pathogens in a single test with the potential for being a new tool for epidemiological studies.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.375

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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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

Citations46
Published2005
Admission routes2
Has abstractyes

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