Strong winter-time deep-water formation during the Little Ice Age in subarctic semi-enclosed formerly glaciated marginal seas (Baltic Sea and Eastern Canadian coastal waters) 
Bibliographic record
Abstract
New hydroacoustic measurements combined with old data reveal the widespread occurrence of contourite drift deposits - indicative of persistent strong bottom currents - at rather great water depths in the northern Baltic Sea and Eastern Canadian coastal waters (Foxe Basin, Hudson Bay, Gulf of St. Lawrence). In addition, lag deposits suggest that strong bottom currents temporary eroded sediments most likely during the cold Little Ice Age. For example, the Little Ice Age lag deposits are found to a water depth of ~ 300 m in Foxe Basin and to ~ 150 m in the Baltic Sea. In all ecosystems the depositional environment changed drastically with the onset of climate warming after the Little Ice Age: calm conditions prevailed leading to the accumulation of fine-grained sediments. A possible mechanism to explain the strong bottom currents during the Little Ice Age is an enhanced deep-water formation caused by accelerated convection and/or brine formation (Eastern Canadian waters) during colder winter conditions. Attempts to model the enhanced winter-time deep-water formation / convection remain inconclusive and do not match the hydroacoustic and sedimentological evidence. However, solving this issue is critical as it could allow to, e.g., reconstruct past winter temperatures based on sedimentological grain-size studies. Yet, most proxies used in paleo-oceanographic temperature reconstructions only relate to spring and summer (growing season) conditions. Our results indicate that winter temperature changes (strength and length of sea-ice season) are of critical importance for the depositional environment and bottom water ventilation in the Eastern Canadian and Baltic Sea ecosystems.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".