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Record W2117622367 · doi:10.7202/013966ar

Influence of MCPA on Fusarium oxysporum root rot and red clover growth under controlled greenhouse conditions

2006· article· en· W2117622367 on OpenAlexvenueno aff
Ricardo Ceballos, Graciela Palma, Fernando Perich, Fernando Pardo, Andrés Quiróz

Bibliographic record

VenuePhytoprotection · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMCPARoot rotBiologyFusarium oxysporumRed CloverShootAgronomyMyceliumHorticultureDry weightPhytotoxicityGerminationPesticide

Abstract

fetched live from OpenAlex

Root rot caused by Fusarium oxysporum is a disease that reduces red clover persistence. Agronomical management of red clover includes MCPA application, and there is no information regarding the effects of this herbicide on the disease. MCPA was evaluated for its effects on F. oxysporum root rot and red clover ( Trifolium pratense ) growth in a greenhouse experiment. Additionally, in vitro mycelial growth and conidial germination of F. oxysporum were studied. For shoot dry weight and crown diameter of seedlings, the interaction of herbicide and inoculum was significant at 30 d. The herbicide–inoculum treatment reduced shoot dry weight by 20% at 1X rate and by 24% at 2X rate, and crown diameter was reduced by 10% at the high rate. The MCPA treatment caused a 40% reduction of root dry weight by the end of the experiment. Application of MCPA caused fusarium root rot to increase in severity on red clover seedlings and caused phytotoxicity at the high rate. Interaction with the other growth parameters was not significant, indicating that the effects of herbicide and inoculum were independent. Conidial germination and mycelial growth in vitro were reduced by MCPA. Results suggest that red clover growth could be negatively affected by F. oxysporum after MCPA application and that root rot severity increases at high rates of MCPA.

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 score0.673
Threshold uncertainty score0.432

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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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