River Water Temperature in Relation to Local Air Temperature in the Mackenzie and Yukon Basins
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".