Shelf-Basin Connectivity Drives Dissolved Fe and Mn Distributions in the Western Arctic Ocean: A Synoptic View into Polar Trace Metal Cycling
Bibliographic record
Abstract
There have been many changes over the past few decades in the physical environment and ecosystem health of the Arctic Ocean, which is a sentinel of global warming. Bioactive trace metal data of important micronutrients for algae across the global ocean, such as iron (Fe) and manganese (Mn), are key indicators of biogeochemical change. However, trace metal data in the Arctic have been historically sparse and generally confined to ice-free regions. In 2015, three major GEOTRACES expeditions sought to resolve trace metal distributions across the Arctic, covering the western, eastern, and Canadian Arctic sectors. The diverse Arctic shelves displayed unique controls on Fe and Mn cycling due to differing chemical, biological, and physical properties. Here, we contrast the shallow, reducing Chukchi Shelf in the western Arctic with the tidally forced, advective Canadian Arctic and the deeper, less productive Barents Shelf in the eastern Arctic. Reductive dissolution and physical resuspension both proved to be large sources of Fe and Mn to the Arctic and the North Atlantic outflow. In the isolated intermediate and deep waters, one-dimensional scavenging in the western and eastern Arctic contrasts with vertical biological signals in Baffin Bay and the Labrador Sea.
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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 | 0.000 | 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) | 0.001 | 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".