Deep convection in the Subpolar Gyre, how much data is needed to estimate its intensity?
Bibliographic record
Abstract
Deep convection in the Subpolar Gyre (SPG) of the North Atlantic forms a link between the upper and lower limbs of the Atlantic Meridional Overturning Circulation (AMOC). The intensity of convection is estimated using mixed layer depth (MLD) derived from in situ vertical profiles of potential density. Given limited areas of convective chimneys, the robustness of the estimates from an available set of vertical profiles needs to be verified before studying mechanisms of interannual variability of convection intensity. For reaching this goal, we first computed the frequency of deep convection events observed in situ and split the convective regions into three domains: the central part of the Irminger Sea (I-DC), the southwestern part of the Labrador Sea (L-DC), and a domain south of Cape Farewell (F-DC). For each domain, we identified two types of development of the convective regions using k-means cluster analysis. Then, for each convection domain and each convection type, the minimum number of randomly scattered casts required for a robust estimate of the maximum MLD during the convective period are derived as a criterion of a robust estimate of the convection intensity. The results showed that, for all the convection domains, a sufficient number of casts during a cold season was collected since the late 1990s for some years, while uninterrupted time series are obtained since the mid-2000s. The main modes of spatio-temporal variability of salinity and temperature in the upper North Atlantic, preceding the years with high/low convection intensity, are accessed through constructing the composite maps of their anomalies and the empirical orthogonal function (EOF) analysis. The first EOF of temperature closely corresponds to the composite map of temperature anomalies, while its principal component has a high correlation with interannual variability of convection in the I-DC and F-DC convection domains, and a moderate one in the L-DC domain. At the same time, a high correlation with the SPG index is also observed. The results suggest that the variability of these dynamic patterns may play an important role in shaping convection intensity in the SPG. Funding: The research was funded by Saint Petersburg State University (SPSU), project no. 75295423.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".