CO2 Emission and Change in the Fertility Parameters of a Calcareous Soil Following Annual Applications of Deinking Paper Sludge (The Case of Tunisia)
Bibliographic record
Abstract
The use of deinking paper sludge (DPS) as a fertilizer instead of sending it to landfill could play a role in reducing greenhouse gases and improving soil properties. The objectives of this study were (1) to evaluate the changes in the physical (permeability and structural stability), chemical (particularly soil pH), and biological (microbial metabolic quotient (qCO2), microbial biomass soil CO2 emissions) of a calcareous agricultural soil following two successive annual amendments with three treatments (0, 30, and 60 Mg DPS ha−1—control, DPS30, and DPS60, respectively); and (2) to determine whether the addition of N-fertilizer to these treatments (controlF, DPS30F, and DPS60F, respectively) causes changes to soil fertility. The DPS application increased soil organic matter (+0.80%: DPS60 vs. control; and +0.35%: controlF vs. DPS60F), available phosphorus (+23.14 mg kg−1: DPS60 vs. control; and +14.34 mg kg−1: DPS60F vs. controlF), potassium (+0.6 g kg−1: controlF vs. DPS30F), and calcium (+0.28 g kg−1: DPS60 vs. control). The 60 Mg DPS ha−1 rate improved permeability and structural stability, regardless of the presence or absence of N-fertilizer. On the other hand, the 60 Mg DPS ha−1 rate without N-fertilizer lead to a decrease in total mineralization rate and qCO2, thereby indicating a reduction in CO2 emissions. The rate of 60 Mg ha−1 DPS could be effectively used to enhance the permeability and stability (soil restoration) and mitigate CO2 emissions, whereas the 30 Mg ha−1 rate could be used as fertilizer to improve the fertility of calcareous soils.
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How this classification was reachedexpand
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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".