Landscape and Hydrological Controls on Organic Matter and Surface Water Chemistry in the Niaqunguk (Apex) River Watershed, Nunavut
Bibliographic record
Abstract
Anthropogenic warming in the Arctic is causing shifts in hydrological cycling and permafrost thaw, with implications for water quality and availability in northern communities. In the capital of the Canadian Territory Nunavut, the city of Iqaluit reports declining water levels, further evidence of the challenges many northern and Indigenous communities face in accessing clean, potable water. The Niaqunguk (or commonly known as Apex) River watershed (63° 45' 20" N, 68° 27' 37" W) has been selected to supplement the city’s main reservoir, Lake Geraldine. However, it is uncertain how the water quality of the river will change with further warming. The objectives of this research were to 1) investigate the landscape controls on spatial and temporal stream water chemistry trends and 2) determine the factors influencing dissolved organic matter (DOM) composition and quality. To address these research objectives, stream water samples and discharge measurements were collected several times weekly at three sub-catchments of varying dominant surficial geologies and three sites at the main stem during the warm period (Jun – Aug 2022). Stable water isotope signatures, major ion concentrations, dissolved organic carbon (DOC), total dissolved nitrogen (TN), and DOM absorbance and fluorescence properties were derived from these samples. The rise in geogenic (Ca2+, Mg2+, SO42–, and HCO3–) solutes over the study period suggests enhanced subsurface flows contributing to stream water chemistry. Further, the spring freshet is a significant driver of DOC and terrestrial DOM export. The decline in fluorescence intensity downstream and over the study period indicates photodegradation or microbial decomposition of fluorescent DOM. The results from this study indicate this watershed will be sensitive to changes in the permafrost under a warming climate, with implications for increases in solute and inorganic nutrient export as consequence of permafrost thaw. These results improve our understanding of the controls on stream water chemistry in Arctic catchments, which is critical for strengthening community resilience and adaptation capacity with regards to climate change.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".