Benthic iron flux influenced by climate‐sensitive interplay between organic carbon availability and sedimentation rate in Arctic fjords
Bibliographic record
Abstract
Abstract Benthic iron (Fe) fluxes from continental shelf sediments are an important source of Fe to the global ocean, yet the magnitude of these fluxes is not well constrained. Processing of Fe in sediments is of particular importance in the Arctic Ocean, which has a large shelf area and Fe limitation of primary productivity. In the Arctic fjords of Svalbard, glacial weathering delivers high volumes of Fe‐rich sediment to the fjord benthos. Benthic redox cycling of Fe proceeds through multiple pathways of reduction (i.e., dissimilatory iron reduction and reduction by hydrogen sulfide) and re‐oxidation. There are few estimates of the magnitude and controlling factors of the benthic Fe flux in Arctic fjords. We collected cores from two Svalbard fjords (Kongsfjorden and Lilliehöökfjorden), measured dissolved Fe2+ concentrations using a two‐dimensional sensor, and analyzed iron, manganese, carbon, and sulfur species to study benthic Fe fluxes. Benthic fluxes of Fe2+ vary throughout the fjords, with a “sweet spot” mid‐fjord controlled by the availability of organic carbon linked to sedimentation rates. The flux is also impacted by fjord circulation and sea ice cover, which influence overall mineralization rates in the sediment. Due to ongoing Arctic warming, we predict an increase in the benthic Fe2+ flux with reduced sea ice cover in some fjords and a decrease in the Fe2+ flux with the retreat of tidewater glaciers in other regions. Decreasing benthic Fe2+ fluxes in fjords may exacerbate Fe limitation of primary productivity in the Arctic 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 | 0.001 | 0.000 |
| 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".