5. The Effect of Climate Variability on Lake Water Balances and Water Quality in the Apex River Watershed, Baffin Island, Nunavut
Bibliographic record
Abstract
Iqaluit, Nunavut is currently facing a water shortage as its population rises, and its drinking water reservoir, Lake Geraldine, is increasingly insufficient. The City of Iqaluit is currently looking into alternative sources of drinking water to supplement this reservoir. This study investigates the effects of inter-annual climate variability on the water chemistry of twenty Arctic lakes in a continuous permafrost region, and the potential implications of these changes on drinking water availability. The twenty lakes are located in the Niaqunguk (Apex) river watershed, Baffin Island, Nunavut (which is adjacent to the Lake Geraldine watershed at Iqaluit) were sampled annually during late July between 2014-2017. Ion concentrations and stable isotopes were measured for each lake annually throughout the study period to compare variability between lakes and between years. Water chemical and physical properties are used to gain insight into the inputs and outputs of each lake, and the changes in estimated water balance between years. This information will be used to determine the impact of different hydrological conditions on water chemistry. Results of water isotope tracers of the twenty lakes indicate that there are important year-to-year changes in the water stable isotopes, indicating inter-annual variations in evaporation rates, suggesting that these lakes are sensitive to changes in summer climatic conditions. Preliminary analyses of the ionic concentrations of the lakes suggest that ion concentrations generally decrease throughout the study period, and largely vary together, signifying the hydrological processes controlling the lake water balances are related to climate variability. This research will provide insights into the hydrologic response of lakes in a continuous permafrost region to long-term climatic 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".