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Record W4320493936 · doi:10.1080/07060661.2023.2177352

A status update on fusarium head blight on Western Canadian wheat

2023· article· en· W4320493936 on OpenAlexaffvenueabout
Tiffany Chin, Kerri Pleskach, Sheryl A. Tittlemier, María Antonia Henríquez, Janice Bamforth, Niradha Withana Gamage, Tehreem Ashfaq, Sung Jong Lee, Mayantha Shimosh Kurera, Bhaktiben Patel, Sean Walkowiak

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsUniversity of ManitobaAgriculture and Agri-Food Canada
Fundersnot available
KeywordsIncidence (geometry)FusariumMycotoxinDiseaseVeterinary medicineDisease monitoringMedicineAgronomyEnvironmental healthBiologyGeographyBiotechnologyHorticulturePathologyMathematics

Abstract

fetched live from OpenAlex

Fusarium head blight (FHB) is a devastating disease of wheat in Canada and disease surveillance is a critical component of integrated disease management, disease forecasting, prioritizing research and breeding efforts, grain handling, and cleaning post harvest.We report on the recent trends of FHB in Canadian wheat including incidence and severity from 1995 to 2021 based on data from 208,247 samples.Our results show a steady increase in FHB incidence from 1995, with an epidemic year in 2016, followed by several years of low incidence.Results also indicated that FHB severity has been stable over time, but not always correlated with incidence.Data from the analysis of 26,538 samples from 2018 to 2021 demonstrate a strong positive correlation between the number of Fusarium damaged kernels (severity) and concentrations of the mycotoxin deoxynivalenol.Together, our results provide the most robust survey of FHB in Canada to date, which complement national and international disease monitoring and management efforts.

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)0.003
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.053
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.011
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.027
GPT teacher head0.220
Teacher spread0.193 · 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

Citations18
Published2023
Admission routes3
Has abstractyes

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