The use of fungicide Nova to mitigate infection of <i>Sphagnum</i> by parasitic fungi in the greenhouse
Bibliographic record
Abstract
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 m2) and at three times the recommended dose (3 L/10 m2). 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".