Sewage Sludge Compost as Potting Media Component for ivy Pelargonium (Pelargonium peltatum (L.) L’Her.) Production
Bibliographic record
Abstract
The increasing demand and raising cost of high quality peat for horticultural use have led to search for low cost substrates as an alternative. The source of materials for their production can be various types of industrial, municipal and agricultural waste. Most of them are rich in organic matter and minerals essential for plant growth. The aim of the study was to evaluate the growth and flowering of two ivy pelargonium cultivars (‘Beach’ and ‘Boneta’) grown in the media containing sphagnum peat and composts made from municipal sewage sludge and structure-forming components. Two different types of composts were used, consisting in equal proportions of sewage sludge and straw (SSRS) or leaves (SSL). The composts replaced 25% or 12.5% of sphagnum peat (v/v) in the growth media. A control media was sphagnum peat (100%) supplemented with a mixed fertilizer.It was found that the media containing both types of compost might be useful for growing ivy pelargonium. The most beneficial effect on the growth, foliage, and a decorative value of the pelargonium was observed for the medium containing 12.5% of SSL compost and 87.5% of peat. Decorative value of the pelargonium grown in the medium with 25% of SSL compost or with either dose of SSRS compost, did not differ from the control plants. The investigated cultivars differed in the number of shoot, color and area of leaves as well as length of stem of inflorescence. ‘Boneta’ cv. developed more stems and had greener leaves than those from ’Beach’ cultivar. While cultivar ‘Beach’ had greater area of leaves per plant and longer stem of inflorescence.
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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".