Relationships between variations of the land-ocean-atmosphere system of northeastern Asia and northwestern North America
Bibliographic record
Abstract
This study is a broad-scale synthesis of information on climate changes in two Arctic terrestrial regions, eastern Siberia and the Alaska-Yukon area of North America. Over the past 60 years (1951-2010), the trends of temperature and precipitation in the two regions are broadly similar in their seasonality. However, atmospheric advection influences the two regions differently during winter. The differential advective effects are much weaker in the other seasons. The Pacific Decadal Oscillation is the strongest correlator with interannual variability in the two regions, followed by the Arctic Oscillation and the El Nino/Southern Oscillation.Projected changes by the late 21st Century are qualitatively similar to the changes that have been ongoing over the past 60 years, although the rate of change increases modestly under mid-range forcing scenarios (e.g., the A1B scenario). The greatest warming is projected to occur farther north over the Arctic Ocean in response to sea ice loss. Precipitation is projected to increase by all models, although increases in evapotranspiration preclude conclusions about trends toward wetter or drier land surface conditions. A notable feature of the future climate simulations is a strong maximum of pressure decreases in the Bering Sea region, implying further advective changes.
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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 | 0.000 | 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".