Mulches and Biosolids Affect Vigor, Yield and Leaf Nutrition of Fertigated High Density Apple
Bibliographic record
Abstract
`Spartan' apple ( Malus × domestica Borkh.) trees on M.9 (T337) rootstock were planted in April 1994 at 1.25 m × 3.5 m spacing. Seven soil management treatments were applied within a 2-m-wide strip centered on the tree row and arranged in a randomized complete-block experimental design. Treatments included a weed-free strip (check) maintained with four annual applications of glyphosate; surface application of 45 t·ha -1 of Greater Vancouver Regional District (GVRD) biosolids applied in 1994 and again in 1997; mulches of shredded office paper; alfalfa ( Medicago sativa L.) hay; black woven polypropylene; and shredded paper applied over 45 t·ha -1 GVRD-and Kelowna-biosolids applied in 1994 and 1997. All experimental trees were fertigated with phosphorus (P) in the first year and with nitrogen (N) annually. Cumulative yield for the first five harvests was higher for trees subjected to any soil management treatment relative to check trees. Maximum cumulative yield, exceeding check trees by 80%, was measured for trees grown with a shredded paper mulch with or without biosolids application. Trees from the three shredded paper treatments were the only ones significantly larger than check trees after six growing seasons. No increases in leaf nutrient concentration were consistently as sociated with improved tree performance. Notable effects included increased leaf P concentration associated with biosolids application, increased leaf K concentration after alfalfa mulch application and temporary increases in leaf Zn and Cu concentration associated with application of biosolids high in Zn and Cu. Use of both mulches and biosolids amendments benefits growth of trees in high density plantings despite daily drip irrigation and annual fertigation.
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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".