Investigation on the effects of climate change on stream temperature and river ice thickness in the Nelson-Churchill and Mackenzie River basins
Bibliographic record
Abstract
The IPCC (Intergovernmental Panel on Climate Change) has predicted a median increase of 2.4 to 3.5 ℃ of median air temperature by 2100. The Arctic is predicted to be more severely affected due to arctic amplification, where higher latitudes are warming at a rate faster than the global mean. In Canada’s North, observed data are not readily available due to the remote nature of rivers, and accessibility issues. Therefore, modelling offers a solution as a way to estimate variables in remote locations. This project seeks to investigate the effects of climate change on stream temperature and river ice thickness in the Nelson–Churchill and Mackenzie River basins. These basins cover roughly one third of Canada and both drain to the Arctic Ocean. The Arctic HYPE model was used to model five climate scenarios from 1981 to 2070, continuously. The Mann–Kendall trend test was used to determine the stream temperatures and river ice thickness trends, based on pre–whitened yearly mean data. Overall, stream temperatures are expected to increase at a mean rate of 0.022 ℃/year, increasing more quickly in the southern portions of the study area, and disproportionately in the summer months, at an average rate of 0.041 ℃/year. River ice thickness is expected to decrease at an average rate of 0.22 cm/year, and the number of days with river ice cover are expected to decrease at an average rate of 0.4 days/year. Due to increases in stream temperature, cold–water fish species will likely thrive, which would result in disrupting the ecosystem balance. Ocean and major lake inflows are expected to increase in temperature, causing potential harm to ice cover, flora and fauna, and processes such as mixing and stratification in these areas. Ice roads will be affected by shorter frozen river seasons and an overall decrease in ice thickness. Remote communities, and in particular Indigenous communities, will be affected by the decrease in river ice as they disproportionately rely on ice roads for transportation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".