Comparing carbon storage of Siberian tundra and taiga permafrost ecosystems at very high spatial resolution
Bibliographic record
Abstract
Abstract Permafrost‐affected ecosystems are important components in the global carbon (C) cycle that, despite being vulnerable to disturbances under climate change, remain poorly understood. This study investigates ecosystem carbon storage in two contrasting continuous permafrost areas of NE and East Siberia. Detailed partitioning of soil organic carbon (SOC) and phytomass carbon (PC) is analyzed for one tundra (Kytalyk) and one taiga (Spasskaya Pad/Neleger) study area. In total, 57 individual field sites (24 and 33 in the respective areas) have been sampled for PC and SOC, including the upper permafrost. Landscape partitioning of ecosystem C storage was derived from thematic upscaling of field observations using a land cover classification from very high resolution (2 × 2 m) satellite imagery. Nonmetric multidimensional scaling was used to explore patterns in C distribution. In both environments the ecosystem C is mostly stored in the soil (≥86%). At the landscape scale C stocks are primarily controlled by the presence of thermokarst depressions (alases). In the tundra landscape, site‐scale variability of C is controlled by periglacial geomorphological features, while in the taiga, local differences in catenary position, soil texture, and forest successions are more important. Very high resolution remote sensing is highly beneficial to the quantification of C storage. Detailed knowledge of ecosystem C storage and ground ice distribution is needed to predict permafrost landscape vulnerability to projected climatic changes. We argue that vegetation dynamics are unlikely to offset mineralization of thawed permafrost C and that landscape‐scale reworking of SOC represents the largest potential changes to C cycling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".