Bibliographic record
Abstract
Ocean-atmosphere interactions in the North Atlantic are responsible for heat transports that keep the Nordic region and North Western Europe 5–10°C warmer than the average of the corresponding latitude belt. This is to a large extent due to the ocean’s thermohaline circulation (THC). This circulation is driven by differences in water density, which is a function of temperature (thermo) and salinity (haline) and particularly by convection processes in the northern North Atlantic, especially the Labrador Sea and the Greenland Sea. Therefore, the Greenland Sea has attracted much attention in the marine research community over the past decades. Scientific research in the Greenland Sea has been important to understand: The physical processes generating deep convection The role of sea ice in the deep convection process and biological production Uptake of carbon dioxide from the atmosphere and further transport into the ocean interior Variability in deep convection, especially the decrease in the Greenland Sea convection observed over the recent decades The main incentive behind all these activities has been to understand the role of the Greenland Sea – and the rest of the Nordic Seas (Greenland, Norwegian and Iceland Seas) – in the climate system of the world.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".