Trellising System and Cane Density Affect Yield and Fruit Quality of Red Raspberry
Bibliographic record
Abstract
Trellising and cane density treatments were applied to vigorous raspberry ( Rubus idaeus L.) cv. Titan plants in Southern Ontario to determine their effects on yield, yield potential, and canopy microclimate during the 1997 and 1998 seasons. The trellising treatments compared were hedgerow (control), V-trellis, and single-sided shift-trellis (shift-trellis). Cane densities (9, 16, 23, and 30 canes/m 2 ) were established before budbreak each season. The treatments significantly affected yield and yield components. Path analysis indicated that interrelationships among yield components were significantly affected by trellising system. In the second season, plants trained on the V-trellis yielded more than did those in a hedgerow system, while those trained on a shift-trellis had the lowest yield because of a smaller fruiting framework. Fruit quality was lower on the shifttrellis than in the other two systems. These differences were related to light penetration into the three canopies. While an optimum cane density was not found for any system, the maximum cane density (30 canes/m 2 ) produced the highest yields. Yield potential per cane decreased as cane density increased; however, the increase in cane number compensated for this decrease, resulting in an increase in total yield per unit area. Fruit quality also decreased as cane density increased, but cane density did not affect canopy microclimate.
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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".