Evaluation of phytosanitary products for the management of raspberry late leaf rust [Pucciniastrum americanum (Farl.) Arthur]
Bibliographic record
Abstract
Different phytosanitary products (Actinovate SP, Cabrio EG, Double Nickel 55, Fullback 125 SC, Kumulus DF, Nova, Phostrol, Pristine WG, Serenade Opti, Sirocco, StorOx) registered in Canada for the management of various diseases of horticultural crops but not for the management of late leaf rust (LLR) of raspberry were tested against the latter. Efficacy of each product was first determined in vitro on raspberry leaf discs. Based on in vitro efficacy, tested products can be ranked (from the most to the least effective) as follows: Nova, Sirocco, Fullback 125 SC, Pristine WG, Phostrol, Kumulus DF, StorOx, Cabrio EG, Serenade Opti, and Actinovate SP/Double Nickel 55. Five products (Fullback 125 SC, Kumulus DF, Nova, Phostrol, Sirocco) were further tested in the field. Foliar applications of Fullback 125 SC and Nova significantly reduced disease severity as compared with the control. This study shows that the triazole fungicides Fullback 125 SC and Nova were effective against LLR of raspberry as determined using in vitro and field assays and proposes a fast, inexpensive, and easy in vitro method to be used to select phytosanitary products to be tested for field assays.
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 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) | 0.001 | 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".