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Record W3190521842 · doi:10.1111/efp.12714

Response of balsam poplar to inoculation with<i>Marssonina balsamiferae</i>from Minnesota, USA

2021· article· en· W3190521842 on OpenAlexaffabout
G. R. Stanosz, Denise R. Smith, Tod D. Ramsfield, Bernard G. McMahon, William E. Berguson

Bibliographic record

VenueForest Pathology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsBalsamConidiumBiologyHerbariumBotanySpotsCuttingLeaf spotInoculationFungusHorticulture

Abstract

fetched live from OpenAlex

Abstract Leaf spots of poplar ( Populus ) species and hybrids in North America commonly are caused by three Drepanopeziza species (previously referred to by their asexual morph names in genus Marssonina ). A fourth species, Marssonina balsamiferae , is known only from herbarium specimens and was described from balsam poplar ( Populus balsamifera ) leaves collected in 1966 in Manitoba, Canada. Balsam poplar leaves collected in 2014 in Minnesota, USA bore spots with acervuli and conidia consistent with those of M. balsamiferae , and isolates were obtained from these leaves. Conidial morphology and analysis of nuclear ribosomal ITS sequences distinguished the Minnesota strains from the very common leaf spot pathogen of poplars in the northcentral region of the USA, Drepanopeziza brunnea . Inoculation of rooted balsam poplar cuttings with conidia of M. balsamiferae strains from Minnesota resulted in necrotic leaf spots. Acervuli bearing conidia consistent with those of M. balsamiferae were observed on symptomatic leaves, and the fungus was reisolated from infected tissues. Contemporary recognition of existence and pathogenicity of M. balsamiferae , including presence in the USA, should prompt further investigation of its geographic distribution and relationships with hosts and related fungal species.

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 score0.641
Threshold uncertainty score0.636

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 head0.008
GPT teacher head0.226
Teacher spread0.218 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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