Spatial structure of the temporary variability of the Arctic seas surface temperature
Bibliographic record
Abstract
Interannual oscillations in the surface temperature of the Arctic Ocean and the North Atlantic with the southern boundary (instead “border”) at latitude 55° 25′ N between 1949 and 2007 are investigated based on the MPIOM (Max Planck Institute Ocean Model) solution. It is a free surface ocean model based on primitive equations in the Boussinesq and incompressibility approximations. High-resolution spectra were estimated via fast Fourier transform with a maximum resolution (Welch’s method). Factor analysis method, which makes it possible to identify areas with highly correlated oscillations and reduce the study of the characteristics in question to their analysis in local points, is used to minimize the significant amount of the initial information about monthly average sea surface temperature fields. Аnalysis of the main factors made it possible to identify 10 areas with quasi-synchronous variability of temperature anomalies by including the points correlated with relevant factors with correlation exceeding 0.6. Spectral structure compliance classification revealed that the areas of the Chukchi Sea, the Hudson Bay, the Irminger Sea, and the Labrador Sea have oscillation peak similarities for the periods of 5–6 years and 8–9 years. Central and western areas of the Norwegian Sea, the area affected by the North Atlantic Current, the eastern part of the Norwegian Sea, and some areas of the Kara Sea have similar spectral structure defined by the peaks at the 11-year and 6-year periods. The Baffin Bay with two main peaks at the 16-year and 5–6-year periods, and the central and the western parts of the Barents Sea, where oscillations are similar to the ones in the Chukchi Sea at short periods, and to the ones in the south-eastern part of the Barents Sea and in the eastern part of the Norwegian Sea at 7–8-year periods, stand out significantly. In some cases, spectrum peaks in different areas appear shifted and attenuated, so presumably the frequency characteristics of the temperature signal change as it moves across the water area.
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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".