MétaCan
Menu
Back to cohort
Record W4200254710 · doi:10.1080/07060661.2021.1996063

Surveillance, breeding, and cultural practices to prevent a rust storm on crops and trees

2021· article· en· W4200254710 on OpenAlexaffvenueabout
Gurcharn S. Brar, Nicolas Feau, Upinder Gill, Colin W. Hiebert, Jared M. LeBoldus, Robert Park

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of British Columbia
Fundersnot available
KeywordsRust (programming language)AgricultureBiologyResistance (ecology)Tree breedingPopulationAgroforestryGeographyBiotechnologyEcologyWoody plantMedicineEnvironmental health

Abstract

fetched live from OpenAlex

The rust fungi (Pucciniales) are among the most common and devastating pathogens of field crops and forest tree species worldwide. Given the importance of the rusts in agriculture and forestry, this Special Issue of the Canadian Journal of Plant Pathology brings together a variety of papers on rust disease management and biology. The Special Issue comprises 16 full-length articles on rust pathogens of field crops and white pine. Two are review articles summarizing recent advances in rust pathology, two are first reports of new pathogens/diseases in new geographical territories, and 12 articles present results that advance knowledge of rust pathogens in the areas of pathogen population biology, host—pathogen interactions, and resistance breeding. We trust that the articles assembled in this Special Issue will highlight the power of virulence phenotyping coupled with genetic mapping for building durable rust resistance in staple crops such as wheat, as well as the ongoing need for research on rust fungi in field crops and forest trees.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.936

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 head0.015
GPT teacher head0.249
Teacher spread0.235 · 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 designNot applicable
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

Citations3
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Plant PathologySame topicYeasts and Rust Fungi StudiesFrench-language works237,207