Long-term effect of pulses and nutrient management on soil carbon sequestration in Indo-Gangetic plains of India
Bibliographic record
Abstract
Venkatesh, M. S., Hazra, K. K., Ghosh, P. K., Praharaj, C. S. and Kumar, N. 2013. Long-term effect of pulses and nutrient management on soil carbon sequestration in Indo-Gangetic plains of India. Can. J. Soil Sci. 93: 127–136. As an important component of crop diversification, pulses/legumes are known to improve soil quality through their unique characteristics of biological N2 fixation, root exudates, leaf litter fall and deep root system. Changes in the soil organic carbon pool due to the inclusion of pulses in an upland maize-based cropping system were evaluated after seven cropping cycles. The results indicate that inclusion of pulses in an upland maize-based cropping system improved the total soil organic carbon content, being greater in surface soil (0–0.2 m) and declining with soil depth. Of the four carbon fractions of total soil organic carbon (C frac 1 –C frac 4 ) measured in the upland maize-based system, the most labile C fraction (C frac 1 ) was dominant. Distribution of the carbon pool varie...
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".