Effect of Apple Rootstocks on Average `Gala' Fruit Weight at Four Locations after Adjusting for Crop Load
Bibliographic record
Abstract
The influence of rootstock on average fruit weight was evaluated for a subset of data from a multilocation NC-140 apple [Malus sylvestris (L.) Mill. var. domestica (Borkh.) Mansf.] rootstock trial. Data for eight dwarf rootstocks were collected at four locations for 2 years. Analysis of covariance was used to evaluate the effect of rootstock on average fruit weight when crop density or number of fruit per tree was included in the linear model as a covariate. When number of fruit harvested per tree was used as a covariate, average fruit weight was not affected by rootstock in either year in Ontario. In Michigan and Virginia, rootstock and number of fruit per tree, but not the rootstock × number of fruit interaction, were significant, so common slopes models were used to estimate least squares means for average fruit weight. In general, trees on M.27 and P.1 produced the smallest fruit, and trees on B.9, M.9 EMLA, and Mac.39 produced the largest fruit. In New York the interaction of rootstock × number of fruit was significant, so least squares means were estimated at three levels of number of fruit per tree. Both years, at all levels of number of fruit, trees on M.26 EMLA produced the smallest fruit and trees on M.27 EMLA produced the largest fruit. Average fruit weight was most affected by number of fruit per tree when Mark was the rootstock. In general, results were similar when crop density was used as the covariate, except that trees on M.27 EMLA did not produce small fruit in Michigan and Ontario.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".