Nursery crop response to substrates amended with raw paper mill sludge, composted paper mill sludge and composted municipal waste
Bibliographic record
Abstract
Silverleaf dogwood (Cornus alba L. ‘Argenteo-marginata’), forsythia (Forsythia × intermedia Zab. ‘Lynwood Gold’), and weigela (Weigela florida Bunge A.DC. ‘Red Prince’) were grown in #2 (6-L) containers filled with 100% bark or bark mixed with 20, 40 or 60% by volume each of raw paper mill sludge (RB group), Bio Soil compost containing 100% paper mill sludge (BCB group), Waterdown compost containing 40% paper sludge, 40% chicken manure and 20% sawdust (WCB group), and municipal compost consisting of leaf and yard waste (MCB group). A fifth substrate group (MCH) consisted of 100% hemp chips or hemp chips mixed with the same rates of municipal compost. The containers were trickle-irrigated and fertilized with a controlled-release fertilizer. Regression analysis indicated that growth among the barkamended groups was highest for dogwood and forsythia with WCB, increasing dramatically and peaking at about the 40% rate (68 and 94 g plant -1 aboveground dry weight, respectively). Growth of these species was intermediate with MCB and BCB and least with RB, increasing to rates ≥ 50% in these groups. There was no significant response of dogwood to RB. Growth of weigela increased equally with WCB and MCB substrates up to about 40% (117 g plant -1 ), but was not influenced by varying rates of RB and BCB. With the hemp-amended MCH group, growth of all three species increased to rates ≥ 50% (62, 93, and 116 g plant -1 for dogwood, forsythia and weigela, respectively). Growth of the three species over most rates of all substrate groups was similar to, or exceeded that in 80% bark: 15% peat: 5% topsoil, a proven nursery mix. Aboveground dry weight of all three species was positively correlated with soluble salts concentrations in the substrates sampled at planting and on other sampling dates during the season. Key words: Nursery, ornamentals, waste and compost utilization
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".