Weathering dataset collected from climate-threatened glacial river headwaters on the eastern slopes of the Canadian Rocky Mountains (2019-2021)
Bibliographic record
Abstract
Here we provide a biogeochemical dataset containing weathering-specific parameters that we collected between 2019-2021 from the headwaters of three rivers (Sunwapta-Athabasca, North Saskatchewan, and Bow) which originate from the glacierized eastern slopes of the Canadian Rocky Mountains. Geochemical weathering can be extremely pronounced in glacierized watersheds due to large quantities of fresh glacial flour, which in turn can impact both local and global carbon budgets depending on the type of weathering that occurs. However, despite glaciers serving as hotspots of geochemical weathering globally, we still know little about how the type and magnitude of various geochemical weathering reactions change downriver of glaciers, and how this effect may change seasonally or interannually. Our dataset begins to address this.River sampling sites were visited monthly in 2019 and 2020 during the open water season (OWS), beginning during snowmelt in late May/early June, through peak glacial melt in July/August, then during the receding flow period in September/October. Additional samples were collected twice in winter (December 2019, January 2021) during base flow, but only at sites where it was safe to do so. In general, at each river sampling site and time, atmospheric CO2(g) and dissolved in situ riverine CO2(aq) concentrations were directly measured with a Vaisala CARBOCAP® GM70 Hand-Held CO2 Meter fitted with a 0 - 2000 ppm GMP222 CO2 probe sealed in a tight Teflon sleeve. Using clean field sampling protocols, we also collected samples for the analyses of δ13C-dissolved inorganic carbon (DIC) and Δ14C-DIC; δ13C-particulate inorganic carbon (PIC); sulfate isotopes (δ34S-SO4, δ18O-SO4), and radiogenic strontium (87Sr/86Sr).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".