Seasonal and Geographic Variation in Dissolved Carbon Biogeochemistry of Rivers Draining to the Canadian Arctic Ocean and Hudson Bay
Bibliographic record
Abstract
Abstract The chemical composition of river water can be used to diagnose change on land, while playing a determining role in the ecology and biogeochemistry of riverine‐influenced ocean waters. Despite this, little is known about the seasonal and geographic variability of riverine chemistry throughout much of the Canadian north. Here we assess the chemical composition of a broad suite of rivers draining to the Canadian Arctic Ocean and Hudson Bay using previously unpublished government data. We focus on inorganic and organic carbon (alkalinity and dissolved organic carbon), using paired chemistry and discharge measurements to assess constituent flux. Concentrations and area‐normalized yields vary substantially across the northern Canadian landscape, with dissolved organic carbon typically highest in rivers draining the Hudson Bay Lowland, alkalinity highest in rivers draining Cordillera and Plains terrains, and the ratio of organic to inorganic species highest in rivers draining the Canadian Shield. Yields of major weathering ions show that carbonate weathering—a notable proportion of which may be driven by sulfide oxidation—dominates inorganic carbon delivery from the rivers we assess. Despite the reasonably diverse coverage of the data set, we find that clear gaps exist, including a lack of data through to the present day for many rivers, and a dearth of measurements from the Arctic Archipelago and eastern shores of Hudson Bay. We therefore use a modeling approach to extrapolate fluxes to the full Canadian Arctic drainage basin. Region‐specific differences between our results and previous models reinforce the need for targeted river water chemistry measures throughout the Canadian Arctic domain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".