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Record W2102641469 · doi:10.1139/b11-056

The use of fungicide Nova to mitigate infection of <i>Sphagnum</i> by parasitic fungi in the greenhouse

2011· article· en· W2102641469 on OpenAlexafffundvenue
Jean‐François Landry, Cristina Martínez, Line Rochefort

Bibliographic record

VenueBotany · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSphagnumFungicideBiologyBotanyHorticultureChaetomiumInoculationGreenhousePeatFusariumEcology

Abstract

fetched live from OpenAlex

A common problem when growing Sphagnum mosses in the greenhouse is the propagation of parasitic fungi. Since no clear procedure is available to correct the situation, the aim of this experiment is to give scientists and growers a tool to control fungi invasions in the greenhouse. First, eight fungicides and the effect of temperature were tested on Petri dishes inoculated with two fungi commonly found in Sphagnum: Lyophyllum palustre (Peck) Singer and Chaetomium sp. To assess Sphagnum tolerance to fungicides, the four most efficient treatments were tested on healthy Sphagnum carpet, at maximum and minimum concentrations. Finally, the most promising fungicide, Nova (myclobutanil), was tested on Sphagnum carpets infected by L. palustre and Chaetomium sp. Since the concentration of this fungicide had no effect on biomass accumulation, the maximum concentration (0.54 g/L) was tested. Because of the high absorbency of Sphagnum, Nova was applied at the recommended dose (1 L/10 m 2 ) and at three times the recommended dose (3 L/10 m 2 ). An evaluation of infected Sphagnum individuals was carried out after a frequency of two and three applications. The recommendation for controlling the invasion of Sphagnum by L. palustre and Chaetomium sp. in the greenhouse is the application of Nova fungicide at three times the recommended dose. The frequency of applications had no significant effect.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.660

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 head0.034
GPT teacher head0.234
Teacher spread0.200 · 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

Citations14
Published2011
Admission routes3
Has abstractyes

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