Windows in Arctic sea ice: Light transmission and ice algae in a refrozen lead
Bibliographic record
Abstract
Abstract The Arctic Ocean is rapidly changing from thicker multiyear to thinner first‐year ice cover, with significant consequences for radiative transfer through the ice pack and light availability for algal growth. A thinner, more dynamic ice cover will possibly result in more frequent leads, covered by newly formed ice with little snow cover. We studied a refrozen lead (≤0.27 m ice) in drifting pack ice north of Svalbard (80.5–81.8°N) in May–June 2015 during the Norwegian young sea ICE expedition (N‐ICE2015). We measured downwelling incident and ice‐transmitted spectral irradiance, and colored dissolved organic matter (CDOM), particle absorption, ultraviolet (UV)‐protecting mycosporine‐like amino acids (MAAs), and chlorophyll a (Chl a) in melted sea ice samples. We found occasionally very high MAA concentrations (up to 39 mg m−3, mean 4.5 ± 7.8 mg m−3) and MAA to Chl a ratios (up to 6.3, mean 1.2 ± 1.3). Disagreement in modeled and observed transmittance in the UV range let us conclude that MAA signatures in CDOM absorption spectra may be artifacts due to osmotic shock during ice melting. Although observed PAR (photosynthetically active radiation) transmittance through the thin ice was significantly higher than that of the adjacent thicker ice with deep snow cover, ice algal standing stocks were low (≤2.31 mg Chl a m−2) and similar to the adjacent ice. Ice algal accumulation in the lead was possibly delayed by the low inoculum and the time needed for photoacclimation to the high‐light environment. However, leads are important for phytoplankton growth by acting like windows into the water column.
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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 | 0.000 |
| 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".