Contribution of Snow to Arctic First‐Year and Multi‐Year Sea Ice Mass Balance Within the Last Ice Area
Bibliographic record
Abstract
Abstract We present the δ 18 O isotope composition of 16 multi‐year ice (MYI) and eight first‐year ice (FYI) cores collected during spring from the Lincoln Sea, a region within the Last Ice Area, which is expected to retain MYI longest into the future. Isotopic signatures were used to quantify the contribution of snow to sea ice mass. These estimates yield a higher total snow contribution in MYI (10 ± 5%) than to FYI (4 ± 2%). Converted to Snow Depth Equivalent ( SDE ), MYI had five times larger SDE (0.79 ± 0.38 m) than FYI (0.16 ± 0.11 m). The difference is explained by the contribution of refrozen surface and under‐ice melt ponds as well as superimposed/interposed ice for MYI resulting from higher accumulation of snow over multiple, longer accumulation seasons compared to FYI. Given the impending replacement of MYI by FYI, the large difference in absolute snow contributions between MYI and FYI highlights the potential implications in terms of the redistribution of precipitation‐derived (meteoric) freshwater throughout the Arctic Ocean. Furthermore, we observed high relative snow contributions to ice mass in the surface of one‐third of older FYI cores, which we attributed to flooding and formation of snow‐ice. Our results support the premise that flooding events on FYI may be more prevalent throughout the Arctic Ocean than previously assumed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".