Bibliographic record
Abstract
Arctic landscapes underlain by permafrost are threatened by climate warming and may degrade in different ways, including active layer deepening, thermal erosion, and development of thermokarst features. In Siberian and Alaskan late Pleistocene ice-rich permafrost, rapid and deep thaw processes cause surface subsidence due to loss of ground ice and mobilisation deep organic matter. With thawing, formerly freeze-locked organic matter is remobilized. This contributes to the carbon-climate feedback by reactivation of old carbon as greenhouse gases. The permafrost carbon climate feedback has been a process of global significance in the past and may contribute to acceleration of climate warming. \nIn my research, I studied the carbon pools of the deep and ice-rich Yedoma permafrost, which is widespread in Siberia, Alaska and parts of NW Canada. I led data synthesis efforts and analysed field data to estimate that the Yedoma presently stores between 83±12 and 129±30 Gt of frozen organic carbon. During the last glacial period, such deposits potentially stored about 657 ± 97 Gt of organic carbon. Focusing on the estimates for the present and including deposits in degradation features we found ~398 Gt thaw-susceptible carbon in the Yedoma domain. While the Yedoma domain is covering only 7 % of the permafrost region, it represents more than 25 % of the frozen soil carbon pool of the permafrost zone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".