Potentially mineralizable nitrogen from organic materials applied to a sandy soil: fitting the one‐pool exponential model
Bibliographic record
Abstract
Abstract. Over the last three decades there has been a great increase in the production of waste from urban, industrial and agricultural activity that could be recycled as a source of plant nutrients, and used to enhance soil quality. The use of these materials could partially offset the need for mineral fertilizers, giving both economic and environmental benefits. An incubation experiment was carried out using different organic waste materials applied to a Cambic Arenosol. Air‐dried soil was mixed with increasing amounts of composted solid municipal waste, secondary pulp‐mill sludge, hornmeal, poultry manure, solid phase from pig slurry, and composted pig manure, resulting in applications equivalent to 0, 40, 80, 120, 160 and 200 kg ha −1 of Kjeldahl nitrogen. The samples were incubated for 244 days under a controlled environment of 24 °C and 60% water holding capacity of the soil. The increasing amounts of waste applied always led to a greater amount of potentially available nitrogen present in the soil/waste mixture. Based on the proportion of their active N fraction, wastes were ranked: poultry manure>hornmeal>solid phase from pig slurry>composted pig manure>secondary pulp‐mill sludge>composted municipal solid waste. The results were well described by a one‐pool exponential mineralization model, and mineral N formation was proportional to the quantity of waste applied. Of the wastes tested, those from animal sources showed greater nitrogen mineralisation. Nitrification was rapid, and concentrations of ammonium nitrogen remained relatively small.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".