(60) Rooting Stem Cuttings in Compost Tea and Anaerobic Digestion Wastewater
Bibliographic record
Abstract
Cuttings of sage ( Salviaofficinalis `Tricolor'), currant ( Ribesaureum ), euonymus ( Euonymus fortunei var. vegetus ), and weigela ( Weigela florida `Nana Variegata') were rooted under greenhouse conditions (40% shade) and mist in aerated hydroponic solutions consisting of deionized water, or mixtures of deionized water and nutrients with various levels of electrical conductivity (EC, 0.0625, 0.125, 0.25, and 0.5 dS·m -1 ) from each of three sources: compost tea from municipal solid waste; wastewater from anaerobic digestion of municipal solid waste; and Hoagland's (control) nutrient solution. Despite differences in species response, rooting tended to be similar with the three nutrient sources. Euonymus rooting percentage increased linearly with increasing EC and was similar with all three nutrient sources (common regression curve, 61% rooting at 0.5 dS·m -1 ), as did root length (1.4 cm at 0.5 dS·m -1 ), but root number was unresponsive. Currant rooting percent increased curvilinearly and similarly with nutrient sources (87% calculated maximum rooting at 0.25 dS·m -1 ), but root number and length were unresponsive. Sage rooting percentage and root number also increased curvilinearly and similarly with nutrient sources (common regression curve, 100% rooting at 0.34 dS·m -1 , and 4.1 roots at 0.38 dS·m -1 , respectively), as did also root length with the compost tea and Hoagland's (common curve for these two nutrient sources, 11.0 cm at 0.30 dS·m -1 ), but was unresponsive to wastewater. Weigela was unresponsive to EC or nutrient sources (mean percentage of rooting, 73; root number, 6.5; and root length, 1.9 cm).
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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".