Bibliographic record
Abstract
Received: 2016-06-13 | Accepted: 2016-12-13 | Available online: 2017-12-31 http://dx.doi.org/10.15414/afz.2017.20.04.72-77 The study focuses on looking for answers to the following questions: 1. Is biochar application a suitable solution for reducing CO 2 emissions? 2. What application rate significantly reduces CO 2 production to the atmosphere? 3. Does have the application of enriched biochar a justification in relation to reducing CO 2 production? The experiment was established on Haplic Luvisol at the experimental site of SUA in Nitra (Dolná Malanta), where we measured CO 2 emissions from the soil to the atmosphere under the following treatments: different rates (0, 10, 20 t ha -1 ) of pure biochar (B0, B10 a B20) and enriched biochar (EB10, EB20) combined with different levels of mineral nitrogen at doses of 0, 40 and 80 kg ha -1 (N0, N40, N80). Overall, the average values of CO 2 emissions were lower by 19.8 %, 13.3 %, 12.9 %, 9.4 % and 8.7 % in B10N0, B20N40, B20N0, B20N80 and B10N40 treatments as compared to B0N0 (control) during the studied period. On the other hand, the average values of CO 2 were higher by 20% in B10N80 treatments as compared to control (B0N0). Application of enriched biochar whether individually (EB10N0, EB20N0) or with additional N (EB10N40, EB20N40, EB10N80, EB20N80) increased average CO 2 by 29.7 %, 34.6 %, 36.0 %, 44.9 %, 45.8 % and 53.6 % as compared to control (B0N0). The cumulative CO 2 emissions for the whole studied period (2014) were in the following order from the lowest one B10N0 < B20N0 < B20N40 < B20N80 < B10N40 < B0N0 (control) < B10N80 < EB20N40 < EB20N80 < EB10N80 < EB20N0 < EB10N0 < EB10N40. Keywords: biochar, enriched biochar, N-fertilization, CO 2 emission References ALVAREZ, R. et al. 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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".