Natural copper-binding ligands in the Arctic Ocean. The influence of the Transpolar Drift (GEOTRACES GN04)
Bibliographic record
Abstract
The Arctic Ocean is a unique biogeochemical environment characterized by low salinity surface waters, extensive sea-ice coverage, high riverine inputs, large shelf extension and the long residence time of deep waters. These characteristics determine the distribution of dissolved bio-essential trace metals, such as copper (Cu), and the dissolved organic-binding ligands capable of complexing it. This work reports the concentrations and conditional stability constants of dissolved Cu-binding ligands (LCu and log KcondCu2+L) measured in samples from the Polarstern (PS94) expedition, as part of the international GEOTRACES program (cruise GN04). Full-depth profile stations from the Barents Sea, Nansen Basin, Amundsen Basin and Makarov Basin were analysed by competitive ligand exchange-adsorptive cathodic stripping voltammetry (CLE-AdCSV). The basins and water masses presented a wide range of LCu concentrations (range: 1.40 – 7.91 nM) and log KcondCu2+L values (range: 13.83 – 16.01). The highest variability of Cu-binding ligand concentrations was observed in surface waters (≤200 m), and mean concentrations increased from the Barents Sea and Nansen Basin (2.15 ± 0.31 nM and 1.93 ± 0.35 nM, respectively) to the Amundsen (3.84 ± 1.69 nM) and Makarov Basins (4.40± 2.03 nM). The influence of the Transpolar Drift (TDP) flow path was observed in the Amundsen and Makarov Basins, especially on Cu-binding ligand concentrations (LCu range: 3.96 – 7.91 nM). In contrast, deep waters (>200 m) showed no significant differences between basins and water masses in terms of LCu concentrations (range: 1.45 – 2.78 nM) and log KcondCu2+L (range: 14.02 – 15.46). The presence of strong Cu-binding ligands (log KcondCu2+L>13) in surface waters stabilises the excess of dissolved copper (dCu) transported in the TPD and favours its export to the Fram Strait and Nordic Seas.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".