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Record W2143974682 · doi:10.7202/706020ar

Effects of surface wetness duration, temperature, and inoculum concentration on infection of winter barley by Rhynchosporium secalis

2005· article· en· W2143974682 on OpenAlexafffundvenueabout
G. Xue, Richard B. Hall

Bibliographic record

VenuePhytoprotection · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsUniversité de MontréalUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConidiumHorticultureInoculationHordeum vulgareSporeBiologyAgronomyAnimal scienceBotanyPoaceae

Abstract

fetched live from OpenAlex

The effects of surface wetness duration, temperature, and inoculum concentration on development of scald in winter barley ( Hordeum vulgare ) inoculated with race SOI of Rhynchosporium secalisfrom southern Ontario, Canada were examined. On barley line 'GW8614' sprayed with a spore suspension (2 x 10 5 conidia ml -1 ), wet periods of 2-48 h and constant temperatures of 10-25°C during the wet and dry periods, 10-25°C during the wet period and 20°C during the dry period, or 20°C during the wet period and 10-30°C during the dry period allowed scald to develop 8.3-11.5 d after inoculation. The disease developed most rapidly and most severely when the wet period after inoculation was 48 h and the temperature of the wet period and subsequent dry period was 20°C. Scald did not develop within 14 d following temperatures of 30°C during the wet period or of 5°C during the wet or dry periods. At inoculum densities of 10 2 -10 6 conidia ml -1 , the disease severity index values (0-100 scale) increased from 53 to 100 in line 'GW8614' and from 0 to 90 in cultivar OAC Acton and the latent periods decreased from 13.3 to 7.8 d in line 'GW8614' and from more than 14 to 8.5 d in cv. OAC Acton. This information should facilitate screening of barley for resistance to scald.

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 score0.062
Threshold uncertainty score0.368

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 head0.003
GPT teacher head0.199
Teacher spread0.196 · 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

Citations9
Published2005
Admission routes4
Has abstractyes

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