A Comprehensive Biogeochemical Assessment of Climate‐Threatened Glacial River Headwaters on the Eastern Slopes of the Canadian Rocky Mountains
Bibliographic record
Abstract
Abstract Climate change is driving the loss of alpine glaciers globally, yet investigations about the water quality of rivers stemming from them are few. Here we provide an overview assessment of a biogeochemical data set containing 200+ parameters that we collected between 2019 and 2021 from the headwaters of three such rivers (Sunwapta‐Athabasca, North Saskatchewan, and Bow) which originate from the glacierized eastern slopes of the Canadian Rocky Mountains. We used regional hydrometric data sets to accurately model discharge at our 14 sampling sites. We created a Local Meteoric Water Line (LMWL) using riverine water isotope signatures and compared it to collected regional rain, snow, and glacial ice signatures. Principal component analyses of river physicochemical measures revealed distance from glacier explained more data variability than other spatiotemporal factors (i.e., season, year, or river). Discharge, chemical concentrations, and watershed areas were then used to model site‐specific open water season yields for 25 parameters. Chemical yields followed what would generally be expected along river continuums from glacierized to montane altitudinal life zones, with landscape characteristics driving chemical sources and sinks. For instance, particulate chemical yields were generally highest near source glaciers with proglacial lakes acting as settling ponds, whereas most dissolved yields varied by parameter and site. As these headwaters continue to evolve with glacier mass loss, the data set and analyses presented here can be used as a contemporary baseline to mark future change against. Further, following this initial assessment of our data set, we encourage others to mine it for additional biogeochemical studies.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| 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".