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Record W2142464123

DNA MICROARRAYS ON AGAROSE-COATED GLASS SLIDES FOR PLANT PATHOGEN IDENTIFICATION

2005· dissertation· en· W2142464123 on OpenAlexfundno aff
Carol A. Koch

Bibliographic record

VenueSummit (Simon Fraser University) · 2005
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
FundersAgriculture and Agri-Food Canada
KeywordsAgaroseOligonucleotideDNA microarraySubstrate (aquarium)Botrytis cinereaDNAMaterials scienceChemistryChromatographyBiologyGeneBiochemistryGene expressionBotany
DOInot available

Abstract

fetched live from OpenAlex

Agarose-coated glass slides were used as a platform for microarray analysis applied to plant pathogen identification. The agarose substrate combines the desirable features of fluorescence detection and high DNA immobilization capacity, in contrast to nylon membranes, which are unsuitable for fluorescent detection, and standard glass microarray slides, which have a low capacity for immobilization. Oligonucleotide probes were immobilized on the agarose substrate, then hybridized to fluorescently labeled sample DNA. Agarose concentration and hybridizing DNA length affected hybridization efficiency. Probes arrayed on the agarose distinguished Didymella bryoniae and Botrytis cinerea from each other with no cross reaction. No interference from other common greenhouse plant pathogens was found. Results compared favorably with those obtained on nylon membranes, and surpassed those achieved on the commercially available glass substrate. Agarose-coated slides are easily produced, and with the use of a manual arrayer, are an inexpensive alternative to commercial microarrays for small scale applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.008

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreOther

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
Published2005
Admission routes1
Has abstractyes

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