Summer freshwater content variability of the upper ocean in the Canada Basin during recent sea ice rapid retreat
Bibliographic record
Abstract
Freshwater content (FWC) in the Arctic Ocean has changed rapidly in recent years, in response to significant decreases in sea ice extent. Research on freshwater content variability in the Canada Basin, the main storage area of fresh water is very important to understand the input-output freshwater in the Arctic Ocean. The FWC in the Canada Basin was calculated using data from the Chinese National Arctic Research Expeditions of 2003 and 2008, and from expeditions of the Canadian icebreaker Louis S. St-Laurent (LSSL) from 2004 to 2007. Results show that the upper ocean in the Canada Basin became continuously fresher from 2003 to 2008, except during 2006. The FWC increased at a rate of more than 1 m•a−1, and the maximum increase, 7 m, was in the central basin compared between 2003 and 2008. Variability of the FWC was almost entirely limited to the layer above the winter Bering Sea Water (wBSW), below which the FWC remained around 3 m during the study period. Contributors to the FWC increase are generally considered to be net precipitation, runoff changes, Pacific water inflow through the Bering Strait, sea ice extent, and the Arctic Oscillation(AO). However, we determined that the first three contributors did not have apparent impact on the FWC changes. Therefore, this paper focuses on analysis of the latter two factors and the results indicate that they were the major contributors to the FWC variability in the basin.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".