Bibliographic record
Abstract
In 2011, new record high temperatures at 20 m depth were recorded at all permafrost observatories on the North Slope of Alaska, where measurements began in the late 1970s. During the last fifteen years, active-layer thickness has increased in the Russian European North, northern East Siberia, Chukotka, Svalbard and Greenland. Active-layer thickness on the Alaskan North Slope and in the western Canadian Arctic was relatively stable during 1995-2008. The most direct indicators of permafrost stability and changes in permafrost state are the permafrost temperature and the active layer thickness (ALT). Permafrost temperature measured at the depth where the seasonal variations in ground temperature cease to exist is best to use as an indicator of long-term change. This depth varies from a few meters in warm, ice-rich permafrost to 20 m and more in cold permafrost and in bedrock (Smith et al. 2010; Romanovsky et al. 2010a). However, if continuous year-around temperature measurements are available, the mean annual ground temperature (MAGT) at any depth within the upper 15 m can be used as a proxy of the permafrost temperature. The recently concluded International Polar Year (IPY 2007-2009) resulted in significant enhancement of the permafrost observing system in the Arctic; there are now ~575 boreholes (Fig. HTC23; Brown et al. 2010; Romanovsky et al. 2010a). A borehole inventory, including mean annual ground temperatures for most of these boreholes, is available online
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.389 | 0.206 |
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".