Iron Isotope Biogeochemical Cycling in the Western Arctic Ocean
Bibliographic record
Abstract
Abstract The Arctic Ocean is unique, connecting the Atlantic and Pacific basins and being especially vulnerable to the impacts of a changing climate. Iron stable isotopes (δ 56 Fe) provide a unique window into the biogeochemical cycling of Fe in the Arctic. Here we present the first seawater δ 56 Fe for the Western Arctic Ocean, from the 2015 U.S. GEOTRACES GN01 transect. Samples analyzed for δ 56 Fe include seawater dissolved (<0.2 μm), soluble (<∼0.003 μm), and leachable particulate phases. Several key processes were explored using Fe isotopes, each characterized by a distinct combination of δ 56 Fe and Fe concentrations. Input of Fe from reducing continental shelf sediments was characterized by high dissolved Fe concentrations (6.18 ± 4.84 nmol kg −1 ) and low δ 56 Fe (−1.57 ± 0.66‰). Riverine Fe input observed in the Transpolar Drift Current was characterized by high Fe concentrations corresponding to a riverine end‐member Fe concentration of 19 nM, and near‐zero δ 56 Fe (0.02 ± 0.23‰) that was similar to that of average crustal material. The deep Arctic was mostly characterized by low Fe concentrations (0.33 ± 0.14 nmol kg −1 ) and slightly higher δ 56 Fe (0.05 ± 0.30‰), except for samples taken near continental slopes that were affected by sedimentary Fe input with lower δ 56 Fe, and in the Amundsen Basin which showed possible hydrothermal Fe input. Our data thus illuminate Fe biogeochemical cycling processes in the modern Arctic Ocean, and serve as a baseline for understanding how the Arctic Fe cycle responds to climate change.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".