(100) Use of Raw and Composted Paper Mill Sludges, Municipal Waste Composts, and Other Waste Ingredients in Container Nursery Substrates
Bibliographic record
Abstract
Plug-rooted liners of deutzia ( Deutzia gracilis ), dogwood ( Cornus alba `Argenteo-marginata'), forsythia ( Forsythia × intermedia `Lynwood Gold'), and ninebark ( Physocarpus opulifolius ) were grown in 6-L containers. There were 36 different treatment substrates formulated in factorial combinations: two types of paper mill sludge (raw or composted) each at three rates (25%, 33%, or 50%, by volume) mixed with one of three sources of municipal waste compost (cities of Guelph, Toronto, or Waterloo; 25%, 33%, or 50%), and the remainder consisting of one of two base supplements (pine bark or 1-year-old wood chips; 50%, 33%, or 0%). The containers were trickle-irrigated and fertilized with a controlled-release fertilizer. Dogwood (no treatment interaction and responding only to the main effect of compost sources) grew equally well with Toronto and Waterloo composts, but less well with the Guelph compost. Ninebark tended to grow better with Toronto compost, intermediate or similar with Waterloo compost, and least with Guelph compost. Forsythia grew equally well in all bark-based substrates, regardless of sludge type and rate or compost source. With wood-chip-based substrates, however, forsythia grew better with Waterloo than with Guelph compost, and better with raw than with composted sludge when mixed with Toronto compost. Deutzia responded similarly to most substrates, but grew marginally better with raw than with composted paper sludge when Waterloo or Toronto compost was present. Despite these differences in species responses, all plants were of marketable size at the end of the season. There was no sign of nutrient toxicity or deficiency due to any of the substrates.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.000 | 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".