Arctic sea-ice ridges: a major contributor to algal habitable space in spring
Bibliographic record
Abstract
Arctic sea ice is highly heterogeneous and composed of a mosaic of different habitats. Our understanding of the impact of climate change on Arctic sea ice and especially on the ice-associated ecosystems is hindered by both a lack of data and a limited understanding of the processes associated with different sea-ice habitats. In particular sea-ice ridges are one of the most under-sampled and poorly understood components of the Arctic sea-ice system. During a spring campaign in the Arctic Ocean, we combined a number of sampling approaches to quantify: 1) the spatial variability of sea-ice algae at single floe and multiple floe scales; 2) the contribution of ridges to ice algal spatial variability; and 3) the role of ridges in shaping the sea ice as a habitat. For upscaling purposes, algal biomass retrieved from ice cores was compared with biomass estimates based on under-ice profiles covering a total of 36 km. Our results show that the level-ice spatial variability measured on a single ice floe can be representative of the larger scale variability. However, only when ridges are included in the analysis we are able to obtain a comprehensive picture of the large-scale ice algal biomass variability. In spring, ridges let more light pass through the ice due to their geometry and their effects on snow distribution, they thus offer a potentially favorable environment for algae to grow within, and they can act as funnels of light for pelagic organisms. On a large scale, ridges contribute more than 50% percent of the potential habitable space for ice algae for snow-covered Arctic sea ice in spring.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".