Patterns and drivers of carbohydrate budgets in ice algal assemblages from first year <scp>A</scp>rctic sea ice
Bibliographic record
Abstract
Abstract Ongoing changes in sea ice distribution will have major implications for the ecology of the Arctic Ocean. First year ice (FYI) supports abundant ice‐algae communities that produce dissolved and particulate carbohydrates, including extracellular polymeric substances (EPS), which are significant carbon sources, influence ice formation and microbial survival within sea ice, and water column carbon cycling following ice melt. Key drivers of the distribution and composition of these carbohydrates are poorly characterised. Carbohydrates and chlorophyll a concentrations were linearly related in springtime bottom FYI at 36 sites in the Canadian Archipelago region, with high levels of spatial heterogeneity. Nanoeukaryote cell density and phosphate concentration were strong drivers of total and dissolved carbohydrate and uronic acid concentrations. Particulate carbohydrates were strongly related to total bacterial abundance. Dissolved carbohydrates contributed 77% of total carbohydrate: the most abundant (51%) size fraction being dissolved carbohydrates < 8 kDa in size, with dissolved EPS contributing 7% to total carbohydrate. Carbohydrate fractions differed in monosaccharide profiles; dissolved components being glucose rich; particulate EPS containing more mannose, xylose, fucose and arabinose. These profiles corresponded to those of cultured sea‐ice diatoms. Microbial abundance, silicate, nitrate and phosphate concentration and ice thickness were important environmental drivers, with thicker ice containing relatively more particulate EPS, with thinner ice containing high amounts of glucose‐rich smaller‐sized carbohydrate moieties. Changes in ice characteristics will alter the relative balance of labile and refractory carbohydrates generated within bottom ice layers, with implications for food webs and carbon turnover in the warming Arctic Ocean.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".