Species-specific fertilization can benefit container nursery crop production
Bibliographic record
Abstract
Clark, M. J. and Zheng, Y. 2015. Species-specific fertilization can benefit container nursery crop production. Can. J. Plant Sci. 95: 251-262. To determine the responses of six container-grown shrub species to different controlled-release fertilizer (CRF) application rates, plant growth and root-zone traits were evaluated following fertilization with Polyon® 16-6-13, 5-6 month CRF incorporated at 0.60, 0.89, 1.19, 1.49 and 1.79 kg m-3 N. The six species tested at a southwestern Ontario, Canada, nursery were Cornus stolonifera ‘Flaviramea’ (yellow-twig dogwood), Euonymus alatus ‘Compactus’ (dwarf winged euonymus), Hydrangea paniculata ‘Grandiflora’ (Pee Gee hydrangea), Physocarpus opulifolius ‘Nugget’ (Nugget ninebark), Spiraea japonica ‘Magic Carpet’ (Magic Carpet spirea), Weigela florida ‘Alexandra’ (Wine and Roses weigela). Different species responded differently to the CRF rates applied. For the majority of species at the final harvest, growth index, plant height, canopy area, leaf area and above-ground dry weight were greater in high vs. low CRF rates; however, different species had different optimal CRF application rates or ranges: 1.49 kg m-3 N for Hydrangea and Spiraea, 1.19 kg m-3 N for Weigela, 1.19 to 1.49 kg m-3 N for Cornus and Physocarpus, and =0.60 kg m-3 N for Euonymus. Based on these species-specific optimal fertilizer rates or ranges, growers can group plant species with similar fertilizer demands, thereby reducing fertilizer waste and maximizing plant production.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".