Spatiotemporal variations of dissolved organic carbon and carbon monoxide in first-year sea ice in the western Canadian Arctic
Bibliographic record
Abstract
[1] We monitored the spatiotemporal progression of dissolved organic carbon (DOC) and carbon monoxide (CO), along with general meteorological, hydrographic, and biological variables, in first-year sea ice in the western Canadian Arctic between mid-March and early July 2008. DOC and CO concentrations fluctuated irregularly in surface ice, but followed the concentration of ice algae in bottom ice, i.e., low at the start of ice algal accumulation, highly enriched during the peak-bloom and early post-bloom, and depleted again during sea ice melt. Vertical profiles of DOC and CO typically decreased downward in early spring and were variable in the melting season. In the presence of high bottom ice algal biomass in mid-spring, DOC and CO exhibited high concentrations in the bottom (DOC: 563 ± 434 μmol L−1; CO: 82.9 ± 84 nmol L−1) relative to the surface (DOC: 56 ± 26 μmol L−1; CO: 16.8 ± 7 nmol L−1). Landfast ice contained higher levels of DOC and CO than drifting ice. Cruise-mean DOC and CO inventories in sea ice were 87 ± 51 mmol m−2 and 13.9 ± 10 μmol m−2, respectively. Net productions of DOC and CO linked to the ice algal bloom were assessed to be 75 mmol m−2 and 13.2 μmol m−2. Sea ice in the study area was estimated to contribute 7.4 × 107 moles of CO a−1 to the atmosphere. This study suggests that sea ice plays important roles in the cycling of organic carbon and trace gases.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".