Uncertainties in the 20th century carbon budget associated with land use change
Bibliographic record
Abstract
Abstract Uncertainties in the 20th century carbon budget associated with the treatment of land use change (LUC) are assessed using the Canadian Centre for Climate Modelling and Analysis (CCCma) first‐generation Earth System Model (CanESM1). Eight coupled climate carbon cycle simulations are performed using different reconstructions of 1850–2000 land cover derived from historical information on changes in cropland and pasture area. The simulations provide estimates of the emissions associated with LUC, the relative contribution of changes in cropland and pasture to LUC emissions and the uncertainty associated with differences among historical data sets of crop area as well as in the manner in which the historical land cover data are constructed. The resulting estimates of the amount of biomass deforested over the 1850–2000 period range from 63 to 145 Pg C with cumulative implied LUC emissions ranging from 40 to 77 Pg C. These values of LUC emissions are considerably lower than Houghton's estimate of 156 Pg C. The year 2000 atmospheric CO2 concentration ranges between 371.1 ± 3.7 ppm depending on the data set used and the manner in which historical land cover is constructed. This compares to the observed value of 369.6 ppm at Mauna Loa and is 17.3 ± 6.3 ppm larger than for simulations without LUC. Although increases in cropland result in the expected increase in LUC emissions, changes in pasture area decrease these emissions because of carbon sequestration in soils.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".