MétaCan
Menu
Back to cohort
Record W2152375065 · doi:10.1139/x10-045

Clonal evaluation for fusiform rust disease resistance: effects of pathogen virulence and disease escape

2010· article· en· W2152375065 on OpenAlexvenueno aff
Gogce Ceren Kayihan, C. Dana Nelson, Dudley A. Huber, Henry V. Amerson, Timothy L. White, John M. Davis

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsnot available
FundersCooperative State Research, Education, and Extension ServiceU.S. Department of Agriculture
KeywordsBiologyGeneticsPlant disease resistancePathogenVirulencePopulationRust (programming language)HeritabilityHost (biology)Gene

Abstract

fetched live from OpenAlex

We evaluated the precision of phenotypic classification for fusiform rust resistance of Pinus taeda L. in a clonally propagated population segregating for the pathotype-specific resistance gene Fr1. In all marker-defined Fr1/fr1 clones screened with low complexity or ambient inoculum, marker–trait cosegregation was complete with no exceptions. Uncommon exceptions (4 of 30) in which marker-defined Fr1/fr1 clones screened with high complexity inoculum were diseased were probably due to a low frequency of spores virulent to Fr1 resistance. Marker–trait cosegregation for fr1/fr1 clones was less reliable, as all ramets of a few clones (5 of 29, 3 of 25, and 4 of 16) remained disease-free with low complexity, high complexity, or ambient inoculum, respectively. We termed disease-free fr1/fr1 ramets “escapes”, since the genetics of the host–pathogen interaction predicted them to be diseased. For nonmarker-defined materials, we considered escapes to be disease-free ramets within clones that had at least one diseased ramet. Narrow-sense heritability estimates for escape rate were 29% and 23% for the low and high complexity inocula, respectively, suggesting that genetic variation in the host is an important component of this resistance mechanism.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.311
Teacher spread0.293 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicYeasts and Rust Fungi StudiesFrench-language works237,207