Geochemistry of Small Canadian Arctic Rivers with Diverse Geological and Hydrological Settings
Bibliographic record
Abstract
Abstract A survey of 25 coastal‐draining rivers across the Canadian Arctic Archipelago (CAA) shows that these systems are distinct from the largest Arctic rivers that drain watersheds extending far south of the Arctic circle. Observations collected from 2014 to 2016 illustrate the influences of seasonal hydrology, bedrock geology, and landscape physiography on each river's inorganic geochemical characteristics. Summertime data show the impact of coincident gradients in lake cover and surficial geology on river geochemical signatures. In the north and central CAA, drainage basins are generally smaller, underlain by sedimentary bedrock, and their hydrology is driven by seasonal precipitation pulses that undergo little modification before they enter the coastal ocean. In the southern CAA, a high density of lakes stores water longer within the terrestrial system, permitting more modification of water isotope and geochemical characteristics. Annual time‐series observations from two CAA rivers reveal that their concentration‐discharge relationships differ compared with those of the largest Arctic rivers, suggesting that future projections of dissolved ion fluxes from CAA rivers to the Arctic Ocean may not be reliably made based on compositions of the largest Arctic rivers alone, and that rivers draining the CAA region will likely follow different trajectories of change under a warming climate. Understanding how these small, coastal‐draining river systems will respond to climate change is essential to fully evaluate the impact of changing freshwater inputs to the Arctic marine system.
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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 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".