Using Paper De‐inking Sludge to Maintain Soil Structural Form Field Measurements
Bibliographic record
Abstract
A high level of organic matter in soils is crucial to maintain structural stability but organic matter sources differ in their effectiveness in stabilizing structural units. Objectives of this study were, first, to determine the optimal rate of sludge and fertilizer application to improve soil physical properties, and second, to investigate a possible correlation between hydraulic conductivity and structural stability measurements. A 4‐yr field study (1994–1997) was conducted on three different soil types to evaluate the effect of different amounts of de‐inking secondary paper sludge on the soil physical properties. The soil physical properties we monitored were structural stability, water desorption characteristics, bulk density, and saturated hydraulic conductivity. Structural stability was increased by 17% in silty clay soil (SCS) and 15% in loamy soil (LS), but decreased by 35% in sandy loam soil (SLS). Results suggest that the effect of sludge application (SA) is short‐lived and that an annual application of sludge is necessary to obtain a year‐to‐year effect on structural stability. Measured bulk density dropped significantly in the SCS (4–10%) and in the LS (1–6%). A significant increase in air capacity and available water values revealed that SA increases both transmission and storage pores in the SCS. Field‐saturated hydraulic conductivity ( K fs ) was increased in the SCS, but decreased in the SLS and the LS. A good correlation was observed between structural stability and hydraulic conductivity measurements in the SCS and the LS.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".