Characteristics of late summer Arctic brash sea ice and its melting effect on the surface-water biogeochemistry of the Chukchi Shelf and Canada Basin
Bibliographic record
Abstract
To understand the impact of the melting of late summer Arctic brash ice on the surface waters of the Chukchi Sea, we collected sea-ice samples during 2021. Floating sea ice was collected by a wire mesh pallet cage from the side of the R/V Mirai. We measured physical and biogeochemical parameters such as salinity, oxygen stable isotopic ratios, turbidity, and concentrations of chlorophyll-a and nutrients. The samples of brash ice were multiyear ice based on satellite back-trajectory analysis. Comparison of nutrient concentrations in brash ice with those of seawater samples from the temperature minimum layer similar to the water in the sea ice originated suggested that the characteristics of the brash ice were greatly affected by biogeochemical processes such as remineralization. The extremely high turbidity and concentrations of chlorophyll-a observed in the brown/green ice samples reflected the impact of sediment as well as the influence of biological activities. The N:P ratios were less than 1 because of the high phosphate concentrations, even though the ammonium concentrations were high. We hypothesized that this low N:P ratio reflected the combined effects of the accumulation of nutrients due to remineralization in the biofilm and differences of remineralization rate and adsorption features of nitrogen and phosphorus. Based on the high nitrate and ammonium concentrations in the sea-ice samples, we postulated a marked impact of sea-ice meltwater on the nitrogen cycle in the nitrate-depleted surface waters of the Chukchi Sea during late summer. We estimated that meltwater nitrogen could support 0.3%–2.6% of primary production in the northern Chukchi Sea. Our results suggest that high-turbidity ice will play an important role as a source of nutrients to the ocean during melting of sea ice, and understanding its distribution, amount, and geochemical characteristics is vital.
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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 | 0.000 |
| 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".