Surface freshening of the Canada Basin, 2003–2007: River runoff versus sea ice meltwater
Bibliographic record
Abstract
[1] The extent of summer Arctic sea ice has reduced dramatically in recent years and, simultaneously, we have observed surface freshening over the Canada Basin in 2006 and 2007. In order to identify the source of this fresh water, either meteoric or sea ice meltwater, salinity, d18O, and alkalinity were analyzed. Results show that sea ice meltwater increased in the surface water over the central part of the basin in 2006 and 2007, corresponding to the melting of an additional 2.7 m (1.3 m a1) of sea ice. Anomalously fresh surface water observed in the southern part in 2007, however, was mostly attributed to Mackenzie River water extending into the basin interior, a source that was mainly absent in the early 2000s. Comparison with previous data shows that the meltwater component of surface water in the southern part of the Canada Basin has progressively increased at a mean rate of 0.27 m a1 since 1987. This can be explained by a reduction of winter sea ice formation rate by 0.45 m or more during the past two decades. The runoff component showed larger variability in the southern basin but no obvious temporal trend. In the central basin, the river runoff component showed an increasing trend of 0.7 m a1.
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 | 0.003 |
| 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".