Efficacy Testing of Organic Nutritional Products for Ontario Canada Vineyards
Bibliographic record
Abstract
A study was conducted to determine the efficacy of three foliar applied organic fertilizers and their impact on yield, fruit composition, and plant nutrition in mature own-rooted ‘Baco noir’ grapevines in Niagara-on-the-Lake, Ontario, Canada. Three foliar fertilizer products (liquid fish fertilizer, seaweed extract, “Monty's Evergreen”) were applied biweekly as individual treatments as well as in the form of a complete (combination) application at dealer recommended rates from bloom to 2 weeks post-veraison. A control treatment consisted of 150 kg/ha ammonium nitrate (51 kg N/ha) added one week before bloom. Despite using less than 10% of the total N applied in the control, the complete foliar application equalled or surpassed the control in almost all yield, fruit composition, and vine nutrition variables. Despite severely reduced yield due to berry moth patterns experienced in the region in 2005, the complete foliar treatment increased yield by 15%, and this was considered sufficient to justify the increased material costs. The results of this study suggest that the use of foliar fertilizers is effective in replacing soil-applied ammonium nitrate for nutrient supplementation to ‘Baco noir’ grapevines. The implications of this study may cause grape growers in the Niagara Peninsula to reevaluate their nutrient management practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".