Dissolved iron in the Arctic Ocean: Important role of hydrothermal sources, shelf input and scavenging removal
Bibliographic record
Abstract
Arctic Ocean waters exchange with the North Atlantic, and thus dissolved iron (DFe) in the Arctic has implications for the global Fe cycle. We present deep water (>250 m) DFe concentrations of the Central Arctic Ocean (Nansen, Amundsen and Makarov Basins). The DFe concentration in the deep waters varies considerably between these basins, with the lowest DFe concentrations (0.2–0.4 nM) in the Makarov Basin, higher concentrations (∼0.45 nM) in the Amundsen Basin and highest concentrations (∼0.6–0.7 nM) in the Nansen Basin. Atlantic input from the shelf seas and slopes enhances the DFe concentration in the Nansen Basin. Moreover, hydrothermal activity at the Gakkel Ridge causes a significant and widespread enrichment of DFe in the Eurasian Basins, at a depth of 2000–3000 m. Below this maximum, the important role of scavenging and absence of input sources are reflected in a strong relation with dissolved Mn (DMn) and in very low (<0.25 nM) DFe concentrations in the deepest (>3000 m) Amundsen and Makarov Basins. The depth profiles of DFe in the Arctic Ocean, notably in the Makarov Basin, deviate from the DFe distribution pattern observed in other parts of the world 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 | 0.000 |
| 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 | 0.000 |
| 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 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".