Soil carbohydrate and aggregation as affected by carbohydrate composition of paper mill biosolids
Bibliographic record
Abstract
Paper mill biosolids (PB) are recognized as a valuable source of carbon for the physical improvement of arable soils. However, little is known about the composition of carbohydrates of these materials and their breakdown in soil, which contributes to soil structural stability. The objectives of this study were to characterize the carbohydrates in PB and to determine under controlled conditions and in the field the soil carbohydrate content and water-stable aggregation. The field experiment consisted of PB applied every year (2000–2008) at 0, 30, and 60 Mg wet weight·ha−1 to annual row crops with soils collected after 3, 6, and 9 yr. The other experiment consisted of PB added at 50 Mg wet weight·ha−1 to two soils, a clay and a sandy loam, and incubated at 25 °C and 60% water-filled pore space for 16 wk. The PB differed in their content in galactose, mannose, and arabinose for total fraction and sum of carbohydrates for water-soluble fraction. In the field, repeated annual PB application increased most of soil total carbohydrates (sum and individuals) after 3 yr and the proportion of >1 mm stable aggregates. The incubation study confirmed results obtained in the field, where the PB richest in carbohydrates induced the highest increases in soil total carbohydrates in both soil types. Soil total and microbial (galactose and mannose)-derived carbohydrates were closely correlated with the percentage of large aggregates, while with water-soluble carbohydrates, they are highly correlated to the amount of microbial carbohydrates applied, thus further contributing to improve soil C quality.
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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 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) | 0.000 | 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 | 0.001 |
| 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".