Uncertainty analysis of soil organic carbon stock change in Canadian cropland from 1991 to 2001
Bibliographic record
Abstract
Abstract National estimates of changes in the amount of soil organic carbon (SOC) in cropland requires an assessment of uncertainty for accounting and reporting under the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol. Canada has data sets on SOC stocks in croplands, historical changes in SOC levels due to management practices, and historical changes in the area of land devoted to certain soil management practices. We conducted an analysis of uncertainty of the change in SOC levels due to management practices in Canada from 1991 to 2001 using Monte Carlo analysis and a simple model. Probability distribution functions were determined for each of the inputs of the model, enabling us to assess the uncertainty for the output. The storage rate of SOC in cropland soils of Canada for the 10‐year period ranged from 3.2 to 8.3 Mt C yr−1 at 95% confidence, with a mean of 5.7 Mt C yr−1. Approximately 67% (about 3.8 Mt C yr−1) of the increase in SOC storage in Canada occurred in Saskatchewan where the cropland area under no‐till increased from 10% to 35%, and the area of summer‐fallow declined from 43% to 20% during the study period. The large uncertainty in the effect of no‐till on SOC stock changes in the Gray‐Brown Luvisols of Ontario contributed most to the variance in the model output. If trends in agricultural management continue for the next 10‐year census period, the estimated SOC storage would comprise between 7% and 19% of the gap required to achieve the 6% reduction in 1990 greenhouse gas emission levels for Canada under the Kyoto Protocol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".