Dominant patterns of atmospheric variability and \ntheir impact on sea ice concentration \nin the Labrador Sea
Bibliographic record
Abstract
The thesis presents results from an Empirical Orthogonal Functions (EOF) analysis \nof the dominant patterns of atmospheric circulation and their impact on the \nvariability of sea ice in the Labrador Sea. The study uses ERA5 reanalysis of sea \nlevel pressure and sea ice from the European Center for Medium-Range Weather \nForecasting (ECMWF). The analysis focuses on the relationship between the EOFS \nof sea ice concentration and three dominant patterns of the North Atlantic atmospheric \nvariability: the North Atlantic Oscillation (NAO), the Greenland Ridge (GR) \nand the Scandinavia–Greenland pattern (SG). The first EOF of the sea ice concentration \ndetermines the interannual variability of the offshore horizontal sea ice extension. \nThe second EOF is related to the variations in the magnitude of ice concentration in \nthe areas covered by ice every year. A significant correlation was found between the \nNAO and the first sea ice EOF pattern. In particular, the results suggest that the \nquasi-decadal transition in sea ice concentration observed in the mid-1990s was related \nto a quasi-decadal transition from a positive to a negative phase of NAO. The results \nprovide a reference for identifying the drivers of ocean variability in the Labrador Sea \nand their potential impact on the regional marine ecosystem and climate.
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 | 0.001 |
| 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 | 0.001 | 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".