Validation of a delivery strategy for reducing nutrient inputs and improving nutrient use efficiency in greenhouse-grown sub-irrigated pot chrysanthemums
Bibliographic record
Abstract
Two experiments were conducted in a naturally lit research greenhouse to validate our modified strategy for delivering nutrients to sub-irrigated chrysanthemum plants. A split-plot design was used with four blocks arranged randomly, three nutrient rates as the main plot, and two contrasting cultivars as the subplot. The entire nutrient supply was removed at bud break and markedly reduced during vegetative growth, compared to common commercial fertilizer formulations, without adversely affecting tissue nutrient levels and plant/inflorescence quality, indicating that the plants were functioning in the low nutrient sufficiency zone. Specific nutrients (Nt) were more likely to exhibit improved uptake efficiency (shoot Nt content/Nt supply) than improved utilization efficiency (inflorescence DM/shoot Nt content) with decreasing nutrient supply. Thus, the common practice of delivering superfluous nutrient levels to greenhouse-grown chrysanthemum has little scientific merit in terms of nutrient accumulation and plant longevity. Applying a low‐input nutrient delivery strategy to the cultivation of indoor-grown, potted ornamental plants would improve the overall sustainability of the Canadian floricultural industry.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".