A Refinement of the Processes Controlling Dissolved Copper and Nickel Biogeochemistry: Insights From the Pan‐Arctic
Bibliographic record
Abstract
Abstract Recent studies, including many from the GEOTRACES program, have expanded our knowledge of trace metals in the Arctic Ocean, an isolated ocean dominated by continental shelf and riverine inputs. Here, we report a unique, pan‐Arctic linear relationship between dissolved copper (Cu) and nickel (Ni) present north of 60°N that is absent in other oceans. The correlation is driven primarily by high Cu and Ni concentrations in the low salinity, river‐influenced surface Arctic and low, homogeneous concentrations in Arctic deep waters, opposing their typical global distributions. Rivers are a major source of both metals, which is most evident within the central Arctic's Transpolar Drift. Local decoupling of the linear Cu‐Ni relationship along the Chukchi Shelf and within the Canada Basin upper halocline reveals that Ni is additionally modified by biological cycling and shelf sediment processes, while Cu is mostly sourced from riverine inputs and influenced by mixing. This observation highlights differences in their chemistries: Cu is more prone to complexation with organic ligands, stabilizing its riverine source fluxes into the Arctic, while Ni is more labile and is dominated by biological processes. Within the Canadian Arctic Archipelago, an important source of Arctic water to the Atlantic Ocean, contributions of Cu and Ni from meteoric waters and the halocline are attenuated during transit to the Atlantic. Additionally, Cu and Ni in deep waters diminish with age due to isolation from surface sources, with higher concentrations in the younger Eastern Arctic basins and lower concentrations in the older Western Arctic basins.
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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".