Soil Mehlich-3-extractable elements as affected by the addition of biochars to a clay soil co-amended with or without a compost
Bibliographic record
Abstract
Biochar has the potential to sequester carbon and mitigate greenhouse gas emissions, and it may also contribute nutrients for plant growth in temperate climates. Nutrient availability in biochar-amended soil was assessed in a 338 d incubation study. The clay soil prepared with 4% w/w (dry basis) compost or without compost, then amended with wood-based biochar made at different pyrolysis temperatures [maple bark (Acer saccharum) at 400 (M400), 550 (M550), and 700 °C (M700)] on a dry-rate basis of 5% (w/w). After moistening the soil mixture to 44% volumetric soil water content (equivalent to 70% water-filled pore space), soil mixtures were incubated in the dark at 22 °C. Soil was sampled at days 9, 16, 23, 44, 86, 23 170, and 338 of the incubation. Biochar amendment increased the Mehlich-3 phosphorus, potassium (K), magnesium, and copper concentrations, and reduced the Mehlich-3 aluminum and iron concentrations at each sampling date, and M400 had the most significant effect on Mehlich-3-extractable nutrient concentrations. Compost addition also increased the amounts of extractable nutrients. These results suggested that M400 and carbon-rich compost promoted microbial growth and mineralization in amended soil. In addition, soil mixed with compost and amended with biochar had more Mehlich-3-extractable K than when compost or biochar was applied alone, probably due to greater growth and activity of soil K-solubilizing microorganisms. Overall, our study indicated that co-application of wood-based biochar and compost could improve soil fertility in temperate regions by increasing the availability of most plant macronutrients and micronutrients.
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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".