The effect of organic mulches and mycorrhizal substrate on growth, yield and quality of Gold Milenium apples on M.9 rootstock
Bibliographic record
Abstract
Sas-Paszt, L., Pruski, K., Zurawicz, E., Sumorok, B., Derkowska, E. and Gluszek, S. 2014. The effect of organic mulches and mycorrhizal substrate on growth, yield and quality of Gold Milenium apples on M.9 rootstock. Can. J. Plant Sci. 94: 281-291. A 3-yr study was conducted to evaluate the effects of organic mulches and mycorrhizal substrate on growth and yield of apple cv. Gold Milenium grown on M.9 rootstock. Straw (rye), pine bark, conifer tree sawdust, compost (plant debris), cow manure, peat moss substrate (commercial), and mycorrhiza substrate (Mykoflor®, containing mycorrhizal fungi: Glomus intraradices, G. mosseae, G. etunicatum) were applied in spring of each year. All the applied treatments did not affect significantly the tree growth. Mulches did not have a positive effect on total soluble solids of the fruit and the number of fruits in different size categories. Only sawdust mulch significantly increased the number of fruit in size diameter class of 7.0-7.5 cm compared with the control. The use of mulches affected the concentration of macro- and microelements in leaves, particularly Cu, Fe, Mn and Zn. Mulches positively affected the pH and organic matter content of soil. The best results were observed with the use of the compost, cow manure and the mycorrhizal substrate, where the concentrations of P, K and Mg, most of microelements and soil organic matter were elevated.
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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".