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Record W4311927976 · doi:10.1128/mbio.02878-22

Population Genomics Provide Insights into the Global Genetic Structure of <i>Colletotrichum graminicola</i> , the Causal Agent of Maize Anthracnose

2022· article· en· W4311927976 on OpenAlexaff
Flávia Rogério, Riccardo Baroncelli, Francisco Borja Cuevas-Fernández, Sioly Becerra, Jo Anne Crouch, W. Bettiol, M. Andrea Azcárate-Peril, Martha Malapi‐Wight, Véronique Ortéga, Javier Betran, Albert Tenuta, José S. Dambolena, Paul D. Esker, Pedro Revilla, Tamra A. Jackson‐Ziems, Jürg Hiltbrunner, Gary P. Munkvold, Ivica Buhiniček, José Luis Vicente Villardón, Serenella A. Sukno, Michael R. Thon

Bibliographic record

VenuemBio · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsUniversity of GuelphMinistry of Agriculture, Food and Rural Affairs
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesMinisterio de Ciencia e InnovaciónCenter for Gastrointestinal Biology and Disease, School of Medicine, University of North Carolina at Chapel HillNutrition Obesity Research Center, University of North CarolinaConselho Nacional de Desenvolvimento Científico e TecnológicoEuropean CommissionConsejería de Educación, Junta de Castilla y LeónAgencia Estatal de InvestigaciónJunta de Castilla y LeónU.S. Department of Agriculture
KeywordsBiologyGenomicsPopulationPopulation genomicsMycosphaerella graminicolaBiotechnologyEvolutionary biologyComputational biologyGeneticsGenomePathogenGeneMedicine

Abstract

fetched live from OpenAlex

Plant pathogens cause significant reductions in yield and crop quality and cause enormous economic losses worldwide. Reducing these losses provides an obvious strategy to increase food production without further degrading natural ecosystems; however, this requires knowledge of the biology and evolution of the pathogens in agroecosystems.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.293

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 head0.006
GPT teacher head0.217
Teacher spread0.212 · 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

Citations14
Published2022
Admission routes1
Has abstractyes

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