On the geochemical heterogeneity of rivers draining into the straits and channels of the Canadian Arctic Archipelago
Bibliographic record
Abstract
Abstract Ten rivers across northern Canada and the Canadian Arctic Archipelago (CAA) were sampled during spring 2014 and summer 2015 to investigate their geochemical heterogeneity for comparison against larger North American (i.e., Mackenzie and Yukon Rivers) and Siberian rivers. In general, rivers draining the western and/or northern regions of the study area have higher solute concentrations and lower 87 Sr/ 86 Sr ratios compared to rivers draining the eastern and/or southern regions. The inorganic geochemical signatures largely reflect the bedrock geology, which is predominately carbonate in the western and/or northern regions and silicate in the eastern and/or southern regions. Riverine δ 18 O values primarily correlate with latitude, with only a few exceptions. Measurements of total alkalinity (TA) were combined with a regional analysis of bedrock geology and extrapolated to produce a range for the mean characteristic TA of rivers draining into the straits and channels of the CAA (628–819 µeq kg −1 ). Combining this estimate with contributions from the Mackenzie River yields a revised North American river runoff TA of 935–1182 µeq kg −1 , which is much lower than that of the Mackenzie River (1540 µeq kg −1 ). This lower concentration suggests that TA may not be used to distinguish between North American and Siberian river contributions in regions such as Davis Strait.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".