Modern dirty sea ice characteristics and sources: The role of anchor ice
Bibliographic record
Abstract
[1] Extensive dirty ice patches with up to 7 kg m−2 sediment concentrations in layers of up to 10 cm thickness were encountered in 2005 and 2007 in numerous areas across the central Arctic. The Fe grain fingerprint determination of sources for these sampled dirty ice floes indicated both Russian and Canadian sources, with the latter dominating. The presence of benthic shells and sea weeds along with thick layers (2–10 cm) of sediment covering 5–10 m2 indicates an anchor ice entrainment origin as opposed to suspension freezing for some of these floes. The anchor ice origin might explain the dominance of Canadian sources where only narrow flaw leads occur that would not favor suspension freezing as an entrainment process. Expandable clays, commonly used as an indicator of a Kara Sea origin for dirty sea ice, are present in moderately high percentages (>20%) in many circum-Arctic source areas, including the Arctic coasts of North America. Some differences between the Russian and the North American coastal areas are found in clay mineral abundance, primarily the much higher abundance of chlorite in North America and the northern Barents Sea as opposed to the rest of the Russian Arctic. However, sea ice clay mineralogy matched many source areas, making it difficult to use as a provenance tool by itself. The bulk mineralogy (clay and non-clay) does not match specific sources possibly due to reworking of the sediment in dirty floes through summer melting or the failure to characterize all possible source areas.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".