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Record W2128000361 · doi:10.7202/706009ar

Effect of media and temperature on sporulation of Septoria apiicola, and of inoculum density on septoria blight severity in celery

2005· article· en· W2128000361 on OpenAlexaffvenue
I. W. Mudita, Ajjamada C. Kushalappa

Bibliographic record

VenuePhytoprotection · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsConidiumSeptoriaApium graveolensAgarHorticultureBlightBiologySporeBotanyPotato dextrose agarBacteria

Abstract

fetched live from OpenAlex

The number of conidia produced by Septoria apiicola was quantified on seven media incubated at 20°C. Conidia were produced abundantly on celery agar (CA) and celery potato dextrose agar (CPDA) after 12 days of incubation. In general, media containing celery decoction, including cleared V8 juice agar (CVA), produced more conidia than those without celery decoction. The number of conidia and the colony area produced per plate on CA increased with increase in temperature up to an optimum temperature and then decreased markedly. A second-order linear regression equation explained 99% of the variation in the number of conidia produced on CA plates incubated at temperatures ranging from 15 to 30°C, with the predicted optimum temperature of 22.4°C. Ninety eight percent of the variations in the colony area were explained by a linearized BETE-equation with the predicted optimal temperature of 22.7°C. Blight severity in celery ( Apium graveolens var. dulce ) increased with increase in inoculum density. For inoculum density ranging from 5-140 conidia/cm 2 of leaf surface, 92% of the variations in the number of lesions per leaf, pooled from two experiments, were explained by a second-order linear regression equation. The model was less reliable for inoculum densities less than 17 conidia/cm 2 of leaf surface.

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 score0.256
Threshold uncertainty score0.333

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 head0.211
Teacher spread0.208 · 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

Citations1
Published2005
Admission routes2
Has abstractyes

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