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Record W2041641299 · doi:10.5423/ppj.2009.25.1.006

Aggressiveness of Three Snow Mold Fungi on Creeping Bentgrass Cultivars under Controlled Environment Conditions

2009· article· en· W2041641299 on OpenAlexaboutno aff
Seog-Won Chang, Geunhwa Jung

Bibliographic record

VenueThe Plant Pathology Journal · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyCultivarAgrostisInoculationAgrostis stoloniferaGerminationPoaceaeSnowAgronomyHorticultureBotany

Abstract

fetched live from OpenAlex

Snow molds are the most important winter diseases of turfgrass in the United States and Canada. Eight isolates of three snow mold fungal species (three isolates of Typhula ishikariensis, three of T. incarnata, and two of Microdochium nivale) were collected from infected turfgrasses on golf courses. The isolates were evaluated for their relative aggressiveness on three cultivars (L-93, Penncross, and Providence) of creeping bentgrass (Agrostis palustris) under the same controlled conditions. Four plant ages (15, 19, 23 and 27 week-old plants from germination to inoculation) were evaluated for their susceptibility to the three pathogens and for the recovery of the plants. Regardless of age or cultivar of the host plant, M. nivale was found to be more aggressive and faster to infect and colonize than Typhula species. After three weeks recovery, M. nivale-inoculated plants showed higher disease severity than plants inoculated with the two Typhula species. Plants infected by Typhula species displayed no significant difference in disease severity. As creeping bentgrass plants get older, the severity of disease caused by three snow molds gradually decreases. This effect was observed in all cultivars tested, suggesting expression of age-related resistance as the bentgrass plants matured.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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 head0.022
GPT teacher head0.235
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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
Published2009
Admission routes1
Has abstractyes

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