Temporal and spatial variability in sea-ice carbon:nitrogen ratios on Canadian Arctic shelves
Bibliographic record
Abstract
Abstract To enhance the accuracy of carbon cycling models as applied to sea ice in the changing Arctic, we analyzed a large data set of particulate organic carbon (POC) and nitrogen (PON) measurements in first-year bottom sea ice (n = 257) from two Arctic shelves, the Canadian Arctic Archipelago and Beaufort Sea shelf, including dark winter and spring seasonal measurements. Wide ranges of sea-ice POC:PON ratios were observed during both the dark winter (12–46 mol:mol) and spring (3–24 mol:mol) periods. Sea-ice POC:PON ratios and chlorophyll a concentrations were significantly higher in the Archipelago versus the Beaufort Sea shelf (p < 0.01), yet there was a highly significant relationship between sea-ice POC and PON during spring for both shelves (r2 = 0.94). POC:PON ratios were not consistent over the range of measured POC and PON concentrations, justifying the use of a power function model to best describe the relationship between POC and PON. Distinct relationships between POC:PON ratios and chlorophyll-based biomass were observed for the dark winter and the spring: dark winter sea-ice POC:PON ratios decreased with increasing sea-ice biomass whereas spring POC:PON ratios increased with increasing sea-ice biomass. The transition from the dark period to the spring growth period in first-year sea ice represented a distinct stoichiometric shift in POC:PON ratios. Our results demonstrate that the Redfield ratio has limited applicability over the four-order of magnitude range of biomass concentrations observed in first-year sea ice on Arctic shelves. This study emphasizes the need for variable POC:PON stoichiometry in sea-ice biogeochemical models and budget estimates, in particular at high biomass concentrations and when considering seasonality outside of the spring period in first year ice. The use of a power function model for POC:PON relationships in sea ice is also recommended to better constrain carbon estimates in biogeochemical sea-ice models.
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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 | 0.002 | 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".