Carbon Sequestration in Farming Land Soils: an Approach to Buffer the Global Warming and to Improve Soil Productivity
Bibliographic record
Abstract
Global warming associated with arising of atmospheric CO2 is one the most challenging issues today. Agricultural soils behave either a source or a sink of the atmospheric CO2. Based upon the results of soil organic carbon increasing under conservation management, such as reduced- and no-till practice, rotation, and introducing cover crops in many long-term experimental in North America, soil scientists in USA and Canada believe that arable lands here have been changing from the source to the sink of atmospheric CO2. They further believe that carbon sequestrated in North America soils during next 20 years can reach 1.1 billion tons, accounting for 15% of total commission (in year 2008 ~ 2012) of both countries for CO2 emission reduction in the Kyoto Protocol. Hence, soil scientists and related governmental organizations are struggling to introduce the concept of carbon sequestration in soils to the negotiating table of global warming debate. The potential of soil sequestrating carbon is bigger in China than in North America. Management practices, such as conservation tillage, rotation, and cover cropping, widely adopted in North America would not only benefit our degrading soils, but also strengthen China's position in the future global warming negotiation.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".