Modeling Dissolved Pb Concentrations in the Western Arctic Ocean: The Continued Legacy of Anthropogenic Pollution
Bibliographic record
Abstract
Abstract Over the past decade, the international GEOTRACES program has greatly expanded the coverage of dissolved lead (dPb) observations in the western Arctic Ocean including the Canada Basin and the Canadian Arctic Archipelago. However, it is difficult to quantify the drivers of the spatial distribution and seasonal variability of dPb concentrations using observations alone. Here, we present a three‐dimensional model of dPb concentrations in the western Arctic Ocean with experiments from 2002 to 2021 to assess our current understanding of dPb cycling. The dPb model illustrates the impact of current and historical anthropogenic pollution on dPb concentrations in the Arctic Ocean, which accounts for at least 28% of dPb addition to the region, through aerosol deposition and net transport from other ocean basins. Advected water masses from the Pacific and North Atlantic Oceans convey elevated pollution‐derived dPb concentrations to the Arctic and play a key role, contributing 43% to the annual dPb budget. The Labrador Sea is a net source of dPb to Baffin Bay via the West Greenland Current. Within Baffin Bay, simulated dPb concentrations track the seasonal extension of warm Atlantic Water along the West Greenland shelf and occasional dense overflows of Atlantic Water into the deep Baffin Bay interior. While dPb concentrations in the western Arctic Ocean are low, the dPb model simulations presented here show that anthropogenic pollution continues to impact the Pb budget in this region, consistent with recent observational work, and demonstrate the use of dPb as a tracer of Atlantic and Pacific Water masses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".