Optimizing Sulphur Delivery to Sub-irrigated, Pot-grown Chrysanthemums
Bibliographic record
Abstract
Greenhouse floriculture operations pose significant environmental risk due to the extensive use of fertilizer inputs and the generation of nutrient-rich wastewater. Here, we tested the hypothesis that sulphur (S) use efficiency of sub-irrigated, pot-grown chrysanthemums varies with supply and timing of S application. Two split-plot experiments (four blocks) were conducted with disbudded chrysanthemums grown under greenhouse conditions with S treatment (2.25 mmol L-1 supplied continuously over the crop cycle or 2.25, 1.125 and 0.5625 mmol L-1 S supplied during vegetative growth only) as the main plot and cultivar (‘Olympia’ and ‘Covington’) as the sub-plot. Morphological characteristics of plants with fully-expanded inflorescences were unaffected by S treatment. Construction of S and dry mass budgets revealed that S use efficiency increased significantly in both cultivars with decreasing S supply over the crop cycle. This study indicates that S delivery over the crop cycle can be markedly reduced, compared to typical commercial recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".