Pulp and paper mill by-products as soil amendments and plant nutrient sources
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
Pulp and paper mill sludges are produced from primary and secondary treatment of wastes derived from virgin wood fiber sources, recycled paper products, and non-wood fibers. Sludges and sludge composts may be utilized in agriculture to increase soil organic matter, improve soil physical properties, provide nutrients, and increase soil pH. Positive effects of primary, deinking, and low-nutrient combined sludges on soil quality are primarily due to increased soil organic matter, aggregation, water holding capacity, infiltration rate, and cation exchange capacity. Nitrogen and P immobilization are often induced by primary and deinking sludges, but can be overcome by delayed planting, adding N and P, planting of legumes, or composting. Improved crop production obtained with secondary treatment sludges is most often attributable to enhanced nutrient availability, particularly N, but improved soil physical properties are implicated in some studies. Pulp and paper mill sludges and sludge composts are useful soil amendments and plant nutrient sources. Key words: Paper mill sludge, soil physical properties, N and P immobilization, nutrient efficiency, land application
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | high |
| 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 | 0.001 | 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 | 0.001 |
| 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