Initial carbon dynamics of perennial grassland conversion for annual cropping in Manitoba
Bibliographic record
Abstract
Fraser, T. J. and Amiro, B. D. 2013. Initial carbon dynamics of perennial grassland conversion for annual cropping in Manitoba. Can. J. Soil Sci. 93: 379–391. Sequestering atmospheric carbon in agricultural soil is an attractive option for mitigation of rising atmospheric carbon dioxide concentrations. Perennial crops are more likely to gain carbon whereas annual crops are more likely to lose carbon. A pair of eddy covariance towers were set up near Winnipeg Manitoba, Canada, to measure the carbon dioxide flux over adjacent paired perennial grass hay fields with high soil organic carbon. A Treatment field was converted to annual cropping by spraying with herbicide, cutting and tilling. A Control field was cut, but allowed to re-grow. Differences in net ecosystem productivity between the fields were mainly caused by a loss of gross primary productivity in the Treatment field; ecosystem respiration was similar for both fields. When biomass removals and manure applications are included in the carbon budget, the Treatment field lost 149 g C m−2 whereas the Control field sequestered 96 g C m−2, for a net difference of 245 g C m−2 over the June to December period (210 d). This suggests that perennial grass converted for annual cropping can lose more carbon than perennial grassland 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".