Volume and freshwater transports through the Canadian Arcti Archipelago – Baffin Bay system
Bibliographic record
Abstract
water is, after transformation, exported through Davis Strait. With control sections both upstream and downstream Baffin Bay invites the use of an idealized geostrophic approach to estimate transports. The baroclinic transports, driven by the density differences between the Arctic Ocean and Baffin Bay, are first determined. The density and upper layer depth are assumed the same in Lancaster Sound, Nares Strait and the West Greenland Current. Once the baroclinic transports are estimated the sea level difference between the Arctic Ocean and Baffin Bay is computed. Next the upper layer depth in Nares Strait and the West Greenland Current is reduced, while the sea level difference is kept constant. This allows for deep inflows through Nares Strait and the West Greenland Current. To establish a deep outflow through Davis Strait a “barotropic ” sea level slope between the Arctic Ocean and the Labrador Sea is estimated from two “ideal ” water columns. The transports are computed for different salinities in the Polar water and the salinity giving mass balance in the deeper layers is determined. The effects of possible increased melting of the Greenland icecap are examined. If the meltwater is added directly to Baffin Bay the effects are small, but if it is incorporated in the East and West Greenland Current a significant reduction of the outflow through the Archipelago might occur. Citation: Rudels, B. (2011), Volume and freshwater transports through the Canadian Arctic Archipelago–Baffin Bay system, J. Geophys. Res., 116, C00D10, doi:10.1029/2011JC007019.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".