Multi-year and seasonal trends in the water quality of the Niaqunguk River, Nunavut (2013–2018)
Bibliographic record
Abstract
Anthropogenic climate change is modifying the hydrological processes that control fluvial chemistry in Arctic regions.Climatic variability and permafrost thaw have contributed to changes in the timing and intensity of spring snowmelt, the frequency of extreme rainfall events, and lateral flow pathways.Permafrost thaw can increase the connectivity of deeper, subsurface flow pathways with tributary streams and rivers.In combination with increases in snowmelt and rainfall runoff, this can increase the flux of solutes to Arctic rivers.The Niaqunguk River is located on Baffin Island, NU.With no evidence of physical disturbances to the permafrost, the top-down thawing of permafrost is the primary type of permafrost disturbance influencing river chemistry.For the years 2013 to 2018, water samples were collected from the Niaqunguk River up to three times weekly over the course of the flow period and analyzed for major ions, dissolved organic carbon, and total dissolved nitrogen.Water sampling was co-located with a hydrometric station operated by the Water Survey of Canada.Solute fluxes to the Niaqunguk River were driven by the weathering of carbonate minerals by lateral flow pathways.For most years, solute fluxes peaked during spring snowmelt, when flows were the highest.In 2016, however, solute fluxes were highest in mid-summer, following several consecutive days of rainfall.If rainfall is increasing, as is projected for many regions of the Arctic, our data suggest that this could lead to an increase in solute fluxes to and through the Niaqunguk River.In the absence of recent precipitation data, however, it is challenging to determine whether this is the case. 1
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".