21st century change in sea-level pressure investigated in North Pacific Ocean.
Bibliographic record
Abstract
Mean changes in the climatology of 21st century annual variance of the sea-level pressure field in the North Pacific Ocean were investigated from all climate models that were used in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). In several specific areas, a statistically significant increase in the annual variance of sea-level pressure was simulated for the 21st century in three emission scenarios; however, the magnitude of change did not follow the forcing The results indicate increased low-pressure activity for the Bering Sea, Bering Strait, Alaska, Gulf of Alaska, Canadian Rocky Mountains, and the west coast of the U.S. A separate study investigated the potential correlation between the magnitude of change predicted by individual models and the models' equilibrium climate sensitivity value. Statistically significant, positively correlated regions from each emission scenario over the eastern portions of the North Pacific Ocean were found. The results indicated that, in the regions of Alaska, the Gulf of Alaska, the Canadian Rocky Mountains, and the west coast of the U.S., the magnitude of change in the annual variance of sea-level pressure could be predicted by the model's sensitivity to CO2 forcing. The sensitivity and robustness of the results from each study were examined using two different multimodel groups.
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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 | 0.001 |
| 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 | 0.000 | 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".