Dirty sea ice drives higher Mn concentrations in the Canada Basin
Bibliographic record
Abstract
The rapidly changing conditions of the Arctic sea ice system have cascading impacts on the biogeochemical cycles of the ocean. Sea ice transports sediments, nutrients, trace metals, pollutants, and gases from the extensive continental shelves into the more isolated central basins. However, it is difficult to assess the net contribution of this supply mechanism on nutrients in the surface ocean. In this study, we used Manganese (Mn), a micronutrient and tracer which can integrate source fluctuations in space and time, to understand the net impact of the long range transport of sea ice for Mn. We developed a three-dimensional dissolved Mn model within a subdomain of the 1/12 degree Arctic and Northern Hemispheric Atlantic (ANHA12) configuration of NEMO centred on the Canadian Arctic Archipelago, and evaluated this model with in situ observations from the 2015 Canadian GEOTRACES cruises. The Mn model incorporates parameterizations for the contributions from river discharge, sediment resuspension, atmospheric deposition of aerosols directly to the ocean and via melt from sea ice, release of sediment from sea ice, and reversible scavenging, while the NEMO-TOP engine takes care of the advection and diffusion of the tracers. Simulations with this model from 2002 to 2019 indicate that the majority of external Mn contributed annually to the Canada Basin surface is released by sediment from sea ice, much of which originates from the Siberian shelves. Reduced sea ice longevity in the Siberian shelf regions has been postulated to result in the disruption of the long range transport of sea ice by the transpolar drift. This reduced sea ice supply has the potential to decrease the Canada Basin Mn surface maximum and downstream Mn supply, with implications for other nutrients (such as Fe) contained in ice-rafted sediments as well. These results demonstrate some of the many changes to the biogeochemical supply mechanisms expected in the near-future in the Arctic Ocean and the subpolar seas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".