Glaciers and Nutrients in the Canadian Arctic Archipelago Marine System
Bibliographic record
Abstract
Abstract The Canadian Arctic Archipelago (CAA) is vulnerable to climate warming, and with over 300 tidewater glaciers, is a hotspot for enhanced glacial retreat and meltwater runoff to the ocean. In contrast to Greenlandic and Antarctic systems, CAA glaciers and their impact on the marine environment remain largely unexplored. Here, we investigate how CAA glaciers impact nutrient delivery to surface waters. We compare water column properties in the nearshore coastal zone along a continuum of locations, spanning those with glaciers (glacierized) to those without (non‐glacierized), in Jones Sound, eastern CAA. We find that surface waters of glacierized regions contain significantly more macronutrients (nitrogen, silica, phosphorus) and micronutrients (iron, manganese) than their non‐glacierized counterparts. Water column structure and chemical composition suggest that macronutrient enrichments are a result of upwelling induced by rising submarine discharge plumes, while micronutrient enrichments are delivered directly by glacial discharge. Generally, the strength of upwelling and associated macronutrient delivery scales with the subglacial meltwater discharge. Glacier‐driven delivery of the limiting macronutrient, nitrate, is of particular importance for local productivity, while metal delivery may have consequences for regional micronutrient cycling given Jones Sound's important role in modifying water masses flowing into the North Atlantic. Finally, we use the natural variability in glacier characteristics observed in Jones Sound to consider, how nutrient delivery may be affected as glaciers retreat. The impacts of melting glaciers on marine ecosystems through both these mechanisms will likely be amplified with increased meltwater fluxes in the short term, but eventually muted as CAA ice masses diminish.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".