An evaluation of biochar pre-conditioned with urea ammonium nitrate on maize (Zea mays L.) production and soil biochemical characteristics
Bibliographic record
Abstract
Dil, M., Oelbermann, M. and Xue, W. 2014. An evaluation of biochar pre-conditioned with urea ammonium nitrate on maize (Zea mays L.) production and soil biochemical characteristics. Can. J. Soil Sci. 94: 551-562. Biochar can enhance soil fertility, plant nutrient uptake and crop production. Using a potted study, we quantified the effects of adding biochar at 1 t ha-1 (Char), biochar pre-conditioned with urea ammonium nitrate [UAN (Char+)], or UAN only to a control (Contr) with no amendments on maize (Zea mays L.) biomass production, tissue carbon (C) and nitrogen (N) concentrations, N uptake (NU), N utilization efficiency (NUtE), and soil chemistry and biology in coarse-, medium- and fine-textured soils over 6 wk. Soil pH decreased (P<0.05) in Char+ and UAN treatments for all soil textures. Soil organic carbon (SOC) increased (P<0.05) in the coarse and medium textured soil in Char and Char+ treatments. Soil ammonium and soil nitrate were different (P<0.05) among treatments; increasing or decreasing depending upon soil texture. Soil microbial biomass C was lowest (P<0.05) in the UAN treatment for all soil textures. Soil potential microbial activity was significantly greater in the coarse-textured soil in only the Char and Char+ treatments. Maize biomass, tissue N concentration, and NU increased (P<0.05) in soils amended with Char+ or UAN only. NUtE was lower (P<0.05) in Char+ and UAN treatments in the coarse- and medium-textured soils, but this was reversed for the fine-textured soil.
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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) | 0.000 | 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 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".