Variability in river runoff distribution in the Eurasian Basin of the Arctic Ocean
Bibliographic record
Abstract
The distribution of freshwater within the Arctic Ocean and its export from it are intimately involved in climate and climate change processes both within and outside the Arctic Ocean. River runoff in the Arctic Ocean constitutes a major part of the Arctic Ocean freshwater budget. Within the Arctic Ocean, variability in the distribution of river runoff will be reflected in the location of the cold halocline that isolates the sea ice from the warm Atlantic Layer. Outside the Arctic Ocean, such variability will impact on the salinity of North Atlantic waters (Great Salinity Anomaly) and on deep convection areas of the North Atlantic Ocean, and thereby potentially on global thermohaline circulation. Rivers entering the Arctic Ocean have high levels of total alkalinity that contribute significantly to the total alkalinity of the surface Polar Mixed Layer. We exploit total alkalinity data to trace river runoff in the surface Polar Mixed Layer and to observe variability in the river runoff distribution in the Eurasian Basin over the period 1987–2001. The river runoff front changed from a position over the Gakkel Ridge in 1987 and 1991 to over the Lomonosov Ridge in 1996, and returned to a midpoint between the two ridges in 2001. Wind field changes as characterized by the Arctic Oscillation index are considered to be a major factor in determining ice and surface water flow. We note a correlation with 4–6 years delay between changes in river runoff distribution and the Arctic Oscillation index. We show that the delay can be inferred from a geostrophic flow calculation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".