Spatial Complexity in Dissolved Organic Matter and Trace Elements Driven by Hydrography and Freshwater Input Across the Arctic Ocean During 2015 Arctic GEOTRACES Expeditions
Bibliographic record
Abstract
Abstract This study traces dissolved organic matter (DOM) in different water masses of the Arctic Ocean and its effect on the distributions of trace elements (TEs; Fe, Cu, Mn, Ni, Zn, Cd) using fluorescent properties of DOM and the terrigenous biomarker lignin. The Nansen, Amundsen, and Makarov Basins were characterized by the influence of Atlantic water and the fluvial discharge of the Siberian Rivers with high concentrations of terrigenous DOM (tDOM). The Canada Basin and the Chukchi Sea were characterized by Pacific water, modified through contact with productive shelf sediments with elevated levels of marine DOM. Within the surface layer of the Beaufort Gyre, meteoric water (river water and precipitation) was characterized by low concentrations of lignin and tDOM fluorescence proxies as DOM is removed during freezing. High‐resolution in situ fluorescence profiles revealed that DOM distribution closely followed isopycnals, indicating the strong influence of sea‐ice formation and melt, which was also reflected in strong correlations between DOM fluorescence and brine contributions. The relationship of DOM and hydrography to TEs showed that terrigenous and marine DOM were likely carriers of dissolved Fe, Ni, Cu from the Eurasian shelves into the central Arctic Ocean. Chukchi shelf sediments were important sources of dCd, dZn, and dNi, as well as marine ligands that bind and carry these TEs offshore within the upper halocline in the Canada Basin. Our data suggest that tDOM components represent stronger ligands relative to marine DOM components, potentially facilitating the long‐range transport of TE to the North Atlantic.
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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 | 0.001 |
| 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".