Carbon dynamics of perennial grassland conversion for annual cropping
Bibliographic record
Abstract
Sequestering atmospheric carbon in soil is an attractive option for mitigation of rising atmospheric carbon dioxide concentrations through agriculture. Perennial crops are more likely to gain carbon while annual crops are more likely to lose carbon. A pair of eddy covariance towers were set up near Winnipeg Manitoba, Canada to measure carbon flux over adjacent fertilized long-term perennial grass hay fields with high soil organic carbon. In 2009 the forage stand of one field (Treatment) was sprayed with herbicide, cut and bailed; following which cattle manure was applied and the land was tilled. The forage stand in the other field (Control) continued to be cut and bailed. Differences between net ecosystem productivity of the fields were mainly due to gross primary productivity; ecosystem respiration was similar for both fields. When biomass removals and manure applications are included in the carbon balance, the Treatment conversion lost 149 g C m^(-2) and whereas the Control sequestered 96 g C m^(-2), for a net loss of 245 g C m^(-2) over the June to December period (210 days). This suggests that perennial grass converted for annual cropping can lose more carbon than perennial grasses can sequester in a season.
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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) | 0.002 | 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".