Continental and Glacial Runoff Fingerprints in the Canadian Arctic Archipelago, the Inuit Nunangat Ocean
Bibliographic record
Abstract
Abstract Rising temperatures and an acceleration of the hydrological cycle due to climate change are increasing river discharge, causing permafrost thaw, glacial melt, and a shift to a groundwater‐dominated system in the Arctic. These changes are funneled to coastal regions of the Arctic Ocean where the implications for the distributions of nutrients and biogeochemical constituents are unclear. In this study, we investigate the impact of terrestrial runoff on marine biogeochemistry in Inuit Nunangat (the Canadian Arctic Archipelago)—a key pathway for transport and modification of waters from the Arctic Ocean to the North Atlantic—using sensitivity experiments from 2002 to 2020 with an ocean model of manganese (Mn). The micronutrient Mn traces terrestrial runoff and the modification of geochemical constituents of runoff during transit. The heterogeneity in Arctic runoff composition creates distinct terrestrial fingerprints of influence in the ocean: continental runoff influences Mn in the southwestern Archipelago, glacial runoff dominates the northeast, and their influence co‐occurs in central Parry Channel. Glacial runoff carries micronutrients southward from Nares Strait in the late summer and may help support longer phytoplankton blooms in the Pikialasorsuaq polynya. Enhanced glacial runoff may increase micronutrients delivered downstream to Baffin Bay, accounting for up to 18% of dissolved Mn fluxes seasonally and 6% annually. These findings highlight how climate induced changes to terrestrial runoff may impact the geochemical composition of the marine environment, and will help to predict the extent of these impacts from ongoing alterations of the Arctic hydrological cycle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".