River Water Temperature in Relation to Local Air Temperature in the Mackenzie and Yukon Basins
Why this work is in the frame
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Bibliographic record
Abstract
Water temperature has an important impact on many aspects of basin hydrology and ecology. In the northern regions, investigation of river thermal regimes and their changes over space and time is a challenge because of data limitations. This study determines the water temperature regimes at several locations within the Yukon and Mackenzie River basins and examines their relationship with air temperature. The Yukon and Mackenzie Rivers have distinct water temperature dynamics. They remain near zero from freeze-up in the fall to ice breakup in the spring and reach their peak temperature during mid-summer. For the locations examined, peak mean monthly water temperatures ranged from 9˚ to 15˚C, and mean July air temperatures ranged from 13˚ to 16˚C. The lags between water and air temperatures ranged from 1 to 40 days. The largest lag was found at the Great Bear River monitoring location, since water temperature at this site is strongly influenced by the heat storage of Great Bear Lake. Tests of three models, linear regression, logical regression (s-shape), and the physically based air2stream model, show that the air2stream model provided the best results, followed by logical regression. Linear regression gave the poorest result. Model estimates of water temperature from air temperature were slightly improved by the inclusion of discharge data. The water temperature sampling regimes had a considerable effect on model performance; long-term data provide a more robust test of a model. Comparisons of mean monthly water temperatures suggest significant spatial variability and some inconsistency between upstream and downstream sites that is due mainly to differences in data collection schemes. This study strongly demonstrates the need to improve water temperature monitoring in the northern regions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it