Incorporating spatial heterogeneity created by permafrost thaw into a landscape carbon estimate
Bibliographic record
Abstract
The future carbon balance of high‐latitude ecosystems is dependent on the sensitivity of biological processes (photosynthesis and respiration) to the physical changes occurring with permafrost thaw. Predicting C exchange in these ecosystems is difficult because the thawing of permafrost is a heterogeneous process that creates a complex landscape. We measured net ecosystem exchange of C using eddy covariance (EC) in a tundra landscape visibly undergoing thaw during two 6 month campaigns in 2008 and 2009. We developed a spatially explicit quantitative metric of permafrost thaw based on variation in microtopography and incorporated it into an EC carbon flux estimate using a generalized additive model (GAM). This model allowed us to make predictions about C exchange for the landscape as a whole and for specific landscape patches throughout the continuum of permafrost thaw and ground subsidence. During June through November 2008, the GAM predicted that the landscape on average took up 337.1 g C m −2 via photosynthesis and released 289.5 g C m −2 via respiration, resulting in a net C gain of 47.5 g C m −2 by the tundra ecosystem. During April through October 2009, the landscape on average took up 498.7 g C m −2 and released 410.3 g C m −2 , resulting in a net C gain of 87.8 g C m −2 . On average, between the years, areas with the highest permafrost thaw and ground subsidence photosynthesized 17.7% more and respired 3.3% more C than the average landscape. Areas with the least thaw and subsidence photosynthesized 15% less and respired 5.1% less than the landscape on average. By incorporating spatial variation into the EC C estimate, we were able to estimate the C balance of a heterogeneous landscape and determine the collective effect of permafrost thaw on the plant and soil processes that drive ecosystem C flux. In these study years, permafrost thaw appeared to increase the amplitude of the C cycle by stimulating both C release and sequestration, while the ecosystem remained a C sink at the landscape scale.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".