Weekly interactions between sea ice concentrations and the North Atlantic Oscillation in the Odden Sea ice peninsula, Greenland Sea
Bibliographic record
Abstract
In this study, we use principal components analysis and a discrete wavelet decomposition to examine changing patterns of sea ice within the Odden ice peninsula, Greenland Sea. Daily passive microwave satellite imagery collected by the National Snow and Ice Data Center (NSIDC), Colorado have been subset to include ice concentrations within a 22/spl times/40 pixel region located between 72 and 74 degrees North latitude. The Odden is a curious feature because it rapidly changes form in the course of days to weeks, whereby shape is highly dependent upon areas of ice melt and growth. In L. Chasmer (2001) we found numerous connections between the Odden and the North Atlantic Oscillation (NAO), a Northern Hemisphere atmospheric teleconnection pattern. The most prominent similarities occurred seasonally, as is to be expected from the seasonal properties of the NAO. However by examining smaller spatial and temporal changes within the Odden, we find both correspondence and anomalies between the NAO and changing sea ice concentrations. During some weeks/months we find a strong positive correlation between the Odden and the NAO, whereby the NAO leads sea ice development by a few days. However, during other times, we find that the NAO signal may be dampened by other meteorological/oceanographic events, and therefore, the signals are opposite or un-related.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".