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Record W3025831829 · doi:10.5539/jas.v12n6p57

Phytosanitary Quality of Genotypes of Wheat Seeds Used Northern Paraná State

2020· article· en· W3025831829 on OpenAlexvenueno aff
Jacqueline Dalbelo Puia, Leandro Camargo Borsato, Marilize Cristina Gonçalves de Oliveira, Adriano Thibes Hoshino, Marcelo Giovanetti Canteri, Sandra Cristina Vigo

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsPhytosanitary certificationBiologyAlternariaRhizopusBipolarisPenicilliumAspergillusMucorFusariumHorticultureMycotoxinBotanyVeterinary medicineFood scienceMedicine

Abstract

fetched live from OpenAlex

Wheat seeds can be infested and/or infected by microorganisms that might cause deterioration of this propagation structure. The aim of this study was to evaluate the health quality of sixteen wheat genotypes grown in northern Paraná. Therefore, seeds of each genotype were submitted to the blotter test with 16 repetitions, 400 seeds per sample, for phytosanitary quality evaluation. The identification of the fungi was performed based on their morphological characteristics and quantified data. The results revealed variations in incidence, with 20 fungi genera in the analyzed samples. The fungi Rhizopus sp., Aspergillus sp., Penicillium sp. and Bipolaris sp. were found in 100% of the analyzed samples, while Mucor sp. and Alternaria sp. were in 89% and 78% of the samples, respectively. The main pathogens that cause diseases in the aerial part of wheat were not found, or were low incidence in all materials analyzed. The pathogens with the highest incidence associated with wheat seeds were groups of storage fungi and known to produce mycotoxins.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.035
GPT teacher head0.246
Teacher spread0.211 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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