Improving Physical Properties of Organo-Mineral Fertilizers: Substitution of Peat by Pig Slurry Composts
Bibliographic record
Abstract
Some granulated organo-mineral fertilizers (OMF) are made with peat and their utilization is expected to increase mostly because of their advantages over mineral fertilizers. However, peat is a non-renewable resource and could be replaced by sustainable organic materials such as stabilized composted pig slurry. The objectives of this study were to determine the changes of OMF physical properties when 1) substituting peat by composted pig slurry mixtures, 2) changing source of composted mixtures, and 3) increasing the level of organic material in the OMF. Thirty-four mixtures of compost, monoamonium phosphate (MAP), diammonium phosphate (DAP), and peat were granulated in OMF at different proportions. The increase of the compost proportion and the decrease of organic material input (30% vs. 60% of organic materials) improved most physical properties of OMF granule such as bulk and granule densities, total and granular porosities, water content, abrasion fragility, crushing strength, critical relative humidity, and water sorption from moist porous media. In addition, most compost types resulted in similar physical properties of OMF granules. Finding the appropriate organic matter content requires more research and the optimum OMF mixture should be chosen as a function of its combined physical, chemical, and plant response properties.
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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 | 0.000 | 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) | 0.001 | 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".