Tundra landscape heterogeneity, not interannual variability, controls the decadal regional carbon balance in the Western Russian Arctic
Bibliographic record
Abstract
Abstract Across the Arctic, the net ecosystem carbon (C) balance of tundra ecosystems is highly uncertain due to substantial temporal variability of C fluxes and to landscape heterogeneity. We modeled both carbon dioxide (CO 2 ) and methane (CH 4 ) fluxes for the dominant land cover types in a ~100‐km 2 sub‐Arctic tundra region in northeast European Russia for the period of 2006–2015 using process‐based biogeochemical models. Modeled net annual CO 2 fluxes ranged from −300 g C m −2 year −1 [net uptake] in a willow fen to 3 g C m −2 year −1 [net source] in dry lichen tundra. Modeled annual CH 4 emissions ranged from −0.2 to 22.3 g C m −2 year −1 at a peat plateau site and a willow fen site, respectively. Interannual variability over the decade was relatively small (20%–25%) in comparison with variability among the land cover types (150%). Using high‐resolution land cover classification, the region was a net sink of atmospheric CO 2 across most land cover types but a net source of CH 4 to the atmosphere due to high emissions from permafrost‐free fens. Using a lower resolution for land cover classification resulted in a 20%–65% underestimation of regional CH 4 flux relative to high‐resolution classification and smaller (10%) overestimation of regional CO 2 uptake due to the underestimation of wetland area by 60%. The relative fraction of uplands versus wetlands was key to determining the net regional C balance at this and other Arctic tundra sites because wetlands were hot spots for C cycling in Arctic tundra ecosystems.
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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 | 0.000 |
| Open science | 0.001 | 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".