Distribution of soil carbon stocks in Canada's forests and wetlands simulated based on drainage class, topography and remotely sensed vegetation parameters
Bibliographic record
Abstract
Abstract A quasi‐three‐dimensional hydrological model was developed and integrated into the integrated terrestrial ecosystem carbon‐budget model (InTEC V3·0) to improve the estimation of the carbon (C) dynamics in Canadian forests and wetlands. Climate, soil, digital elevation map, and drainage class data, in conjunction with remotely sensed vegetation parameters, including leaf area index, land cover type, and stand age, are used to drive the model. Soil is divided into three layers, for which temperature and moisture dynamics are simulated. Individual 1 km × 1 km pixels are hydrologically linked with neighbouring pixels through subsurface saturated base‐flow, which is simulated using a TOPMODEL‐based scheme. Soil C and nitrogen (N) dynamics are simulated using the soil submodel of CENTURY suitably modified for forests and wetlands. The interannual variation in net primary productivity is iteratively computed after integrating the effects of N, climate, stand age and atmospheric CO 2 concentration on productivity. Compared with data in the Soil Landscape of Canada, the newly updated InTEC V3·0 can capture 66·6% of spatial variations in soil C and effectively alleviate soil C underestimation in wetland areas from its predecessor (InTEC V2·0) by considering the lateral water flow and the water table variation. Copyright © 2005 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".