Deep Water Masses and Sediments Are Main Compartments for Polychlorinated Biphenyls in the Arctic Ocean
Bibliographic record
Abstract
There is a wealth of studies of polychlorinated biphenyls (PCB) in surface water and biota of the Arctic Ocean. Still, there are no observation-based assessments of PCB distribution and inventories in and between the major Arctic Ocean compartments. Here, the first water column distribution of PCBs in the central Arctic Ocean basins (Nansen, Amundsen, and Makarov) is presented, demonstrating nutrient-like vertical profiles with 5-10 times higher concentrations in the intermediate and deep water masses than in surface waters. The consistent vertical profiles in all three Arctic Ocean basins likely reflect buildup of PCBs transported from the shelf seas and from dissolution and/or mineralization of settling particles. Combined with measurement data on PCBs in other Arctic Ocean compartments collected over the past decade, the total Arctic Ocean inventory of ∑7PCB was estimated to 182 ± 40 t (±1 standard error of the mean), with sediments (144 ± 40 t), intermediate (5 ± 1 t) and deep water masses (30 ± 2 t) storing 98% of the PCBs in the Arctic Ocean. Further, we used hydrographic and carbon cycle parametrizations to assess the main pathways of PCBs into and out of the Arctic Ocean during the 20th century. River discharge appeared to be the major pathway for PCBs into the Arctic Ocean with 115 ± 11 t, followed by ocean currents (52 ± 17 t) and net atmospheric deposition (30 ± 28 t). Ocean currents provided the only important pathway out of the Arctic Ocean, with an estimated cumulative flux of 22 ± 10 t. The observation-based inventory of ∑7PCB of 182 ± 40 t is consistent with the contemporary inventory based on cumulative fluxes for ∑7PCB of 173 ± 36 t. Information on the concentration and distribution of PCBs in the deeper compartments of the Arctic Ocean improves our understanding of the large-scale fate of POPs in the Arctic and may also provide a means to test and improve models used to assess the fate of organic pollutants in the Arctic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".