Urban biochar improves nitrogen and phosphorus availability in growing media
Bibliographic record
Abstract
The purpose of this study was to substitute sphagnum peat from plant growing media with urban biochar (UB) and evaluate its impact on nitrogen (N) and phosphorus (P) availability. The UB was produced from the pyrolysis (650°C) of a 2 : 1 ratio of biosolids to green waste. We compared three mixes – 20% sphagnum peat mixed with composted pine bark (B0), 20% UB mixed with composted pine bark (B20) and 60% UB mixed with composted pine bark (B60) – for their ability to promote plant growth and minimise leaching losses in a greenhouse experiment using silverbeet (Beta vulgaris ssp. cicla). Plants were grown in 4.0-L custom-made chambers with the capacity to collect leachate and measure nitrous oxide gas flux. Both biochar mixes increased media pH, air filled porosity, bulk density and nutrient content relative to B0. The B0 had the highest cation exchange capacity and electrical conductivity. The UB-based mixes, B20 and B60, had no significant effect on silverbeet biomass after 11 weeks of growth but had higher N use efficiency and P availability than B0. These results indicate that UB can completely replace sphagnum peat from growing media and can be used at the rate of 60% on volume basis while improving N and P availability. Using a higher rate of biochar in growing media has the additional advantage of sequestering more carbon and reducing urban waste streams and landfill costs.
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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 | 0.001 | 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".