Changes in soil pH and nutrient extractability after co-applying biochar and paper mill biosolids
Bibliographic record
Abstract
Acidification and metal mobility may present challenges in soil receiving paper mill biosolids (PB). Co-applying biochar and PB could help prevent these issues, but its effect must be assessed. The objective of this 224 d incubation study was to evaluate the effect of amending two acidic soils, a clay and sandy loam, with two PB types varying in pH (PB1, pH = 7.80; and PB2, pH = 4.51) co-applied with three rates (0%, 2.5%, and 5% w/w) of pine (Pinus strobus L.) biochar on soil pH and macro- (P, K, Ca, and Mg) and micronutrients (Cu, Zn, Fe, and Mn). In both soils, co-applying biochar and PB significantly increased soil pH and extractable K concentration compared with PB-only application, whereas amending with PB significantly increased soil extractable P concentration compared with the unamended soil. In comparison with PB only, co-applying 5% biochar and PB decreased extractable Cu concentration in both soils and extractable Fe concentration in the sandy loam soil. This study showed that co-applying biochar and PB can be more beneficial to agricultural soils than application of PB alone by supplying nutrients and helping prevent metal toxicity by raising pH, especially in acidic sandy soils.
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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 | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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, 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".