Use of Wastewater and Compost Extracts as Nutrient Sources for Growing Nursery and Turfgrass Species
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nutrient salts present in liquid by-products following waste treatment are lost resources if not effectively recycled, and can cause environmental problems if improperly disposed. This research compared the growth response and mineral nutrient status of two nursery and two turfgrass species, hydroponically supplied with nutritive by-product extracts derived from anaerobically digested municipal solid waste (MSW) and aerobically composted organic wastes from the mushroom and MSW industries. Forsythia (Forsythia x intermedia 'Lynwood') and weigela (Weigela florida 'Red Prince'), and creeping bentgrass (Agrostis palustris Huds.) and Kentucky bluegrass (Poa pratensis L.), were grown in nutrient solutions/extracts prepared from: (i) half-strength Hoagland's #2 solution (HH; control), (ii) Plant Products liquid fertilizer (PP; g kg(-1): 180 N; 39 P; 224 K), (iii) spent mushroom compost (SMC), (iv) MSW compost (GMC), and (v) intra-process wastewater from the anaerobic digestion of MSW (ADW). Additional nutrient solutions (SMC-A, GMC-A, and ADW-A) were prepared by amending the original solutions with N, P, and/or K to concentrations in HH (mg L(-1): 105 N; 15 P; 118 K). Plants receiving the SMC-A extract grew best or at least as well as those in HH, PP, and the amended GMC-A and ADW-A solutions. This study indicated that, with proper amendments of N, P, K and other nutrients, water-soluble constituents derived from organic waste treatment have potential for use as supplemental nutrient sources for plant production.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it