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Record W1967841131 · doi:10.1111/ppa.12351

Inheritance of maize resistance to gibberella and fusarium ear rots and kernel contamination with deoxynivalenol and fumonisins

2015· article· en· W1967841131 on OpenAlexafffund
Ana Butrón, L. M. Reid, Rogelio Santiago, Ana Cao, Rosa Ana Malvar Pintos

Bibliographic record

VenuePlant Pathology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsAgriculture and Agri-Food Canada
FundersEuropean Social FundAgriculture and Agri-Food CanadaCanadian Field Crop Research AllianceMinisterio de Economía y Competitividad
KeywordsBiologyFusariumMycotoxinFumonisinGibberellaResistance (ecology)HybridContaminationAgronomyVomitoxinBiotechnologyZearalenoneBotanyEcology

Abstract

fetched live from OpenAlex

The objective of this study was to investigate the stability, across well‐differentiated environments, of genetic control of maize resistance to Fusarium graminearum and Fusarium verticillioides ear rots and mycotoxin contamination, found in genotypes of diverse origin and adapted to different environments. This knowledge will help to design the most appropriate breeding programme to reduce mycotoxin content across a wide range of environments. Although maize genetics involved in resistance to ear rots and mycotoxin contamination greatly depended on the environment, additive and dominance effects were the predominant genetic effects in most environments. The stability across environments for resistance to ear rots and deoxynivalenol and fumonisin contamination was low, and recommended target areas of breeding programmes for either Fusarium species are different based on the different nature of genetic effect × environment interactions for each species. In general, the classification of inbreds and hybrids according to their resistance levels was similar across environments, suggesting that the same sources of resistance could be suitable for different environments, and breeding for resistance to one species would affect resistance to the other one.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.159

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 head0.018
GPT teacher head0.193
Teacher spread0.175 · 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 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

Citations41
Published2015
Admission routes2
Has abstractyes

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