Modeling soil thermal and carbon dynamics of a fire chronosequence in interior Alaska
Bibliographic record
Abstract
In this study, the dynamics of soil thermal, hydrologic, and ecosystem processes were coupled to project how the carbon budgets of boreal forests will respond to changes in atmospheric CO 2 , climate, and fire disturbance. The ability of the model to simulate gross primary production and ecosystem respiration was verified for a mature black spruce ecosystem in Canada, the age‐dependent pattern of the simulated vegetation carbon was verified with inventory data on aboveground growth of Alaskan black spruce forests, and the model was applied to a postfire chronosequence in interior Alaska. The comparison between the simulated soil temperature and field‐based estimates during the growing season (May to September) of 1997 revealed that the model was able to accurately simulate monthly temperatures at 10 cm ( R > 0.93) for control and burned stands of the fire chronosequence. Similarly, the simulated and field‐based estimates of soil respiration for control and burned stands were correlated ( R = 0.84 and 0.74 for control and burned stands, respectively). The simulated and observed decadal to century‐scale dynamics of soil temperature and carbon dynamics, which are represented by mean monthly values of these variables during the growing season, were correlated among stands ( R = 0.93 and 0.71 for soil temperature at 20‐ and 10‐cm depths, R = 0.95 and 0.91 for soil respiration and soil carbon, respectively). Sensitivity analyses indicate that along with differences in fire and climate history a number of other factors influence the response of carbon dynamics to fire disturbance. These factors include nitrogen fixation, the growth of moss, changes in the depth of the organic layer, soil drainage, and fire severity.
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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 | 0.001 |
| 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 | 0.001 | 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 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".