Bibliographic record
Abstract
Large parts of (sub-)Arctic Siberia, Alaska and the Yukon Territory are covered by ice-rich silts that are penetrated by large ice wedges, resulting from syngenetic sedimentation and freezing. Accompanied by wedge-ice growth, the sedimentation process was driven by cold continental climatic and environmental conditions in unglaciated regions during the late Pleistocene (Schirrmeister et al. 2013) inducing the accumulation of the unique Yedoma deposits up to 50 m thick. \nBecause of fast incorporation of organic material into permafrost during sedimentation, Yedoma deposits are supposed to include low-degraded organic matter. Moreover, ice-rich permafrost deposits like Yedoma are especially prone to degradation triggered by climate changes or human activity. When Yedoma deposits degrade, large amounts of sequestered carbon as well as other nutrients are released to the geosystem, which is of global significance for the climate system. \nIn particular, several new estimates have emerged for carbon that accumulated in yedoma deposits in Siberia and Alaska. The current estimates range from 83±12 (Strauss et al. 2013) to 129±30 Pg (Walther et al. 2014) for organic carbon in Yedoma deposits. The yedoma region, including degraded Yedoma deposits and Holocene accumulation, is now thought to contain between 213±41 Pg carbon (Strauss et al. 2013) and 456±45 Pg frozen carbon (Walter Anthony et al. 2014), still supporting earlier accounts of several hundred billion tons of carbon stored deep in the permafrost, even when recalculated with new observations. \nThe major uncertainties for estimating the Yedoma carbon stock that remain are (1) Yedoma coverage, (2) Yedoma thickness, and (3) Yedoma ground ice (wedge and segregated ice) content.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".