Bibliographic record
Abstract
The Overturning in the Subpolar North Atlantic Program (OSNAP) was initiated in the spring of 2010 through a collaborative effort involving the USA, the UK, Germany, the Netherlands and Canada. A key feature of OSNAP is a trans-basin observing system deployed in the summer of 2014 for the continuous measure of volume, heat and freshwater fluxes in the subpolar North Atlantic. This review focuses on advancements made possible by the collective OSNAP observations. Chief among those advancements is the quantification of the dominant role of the eastern subpolar North Atlantic in the production of dense waters that reside in the lower limb of the overturning: the Irminger and Iceland basins contributed approximately three times as much dense water compared with the Labrador Sea over the observational period. Other advancements include elucidation of the relationship between convective activity in the basin interior and boundary current anomalies; the spread of overflow waters in the subpolar region; the seasonality of the meridional volume, heat and freshwater fluxes; and the challenges involved in designing a simpler, less costly observing system. Collectively, OSNAP measurements are laying a framework on which to assess the overturning circulation's vulnerability to continued warming and freshening as climate change continues apace. This article is part of a discussion meeting issue 'Atlantic overturning: new observations and challenges'.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".