Hydrologic Effects of Climate Change in the Yukon River Basin
Bibliographic record
Abstract
A monthly water balance (WB) model was developed for the Yukon River Basin (YRB). The WB model was calibrated using mean monthly values of precipitation and temperature derived from the Precipitation-elevation Regression on Independent Slopes Model (PRISM) data set and by comparing estimated mean monthly runoff with runoff measured at Pilot Station, Alaska. Potential hydrologic effects of climate change were assessed for the YRB by imposing changes in precipitation and temperature derived from selected Inter-governmental Panel for Climate Change (IPCC) climate simulations. Scenarios from five general circulation model (GCM) simulations were used to provide a range of potential changes. Results from the scenarios indicate an increase in annual runoff in the 21st century for the YRB with simulated increases in precipitation having the greatest effect on increases in runoff. Simulated increases in temperature were found to alter the timing of snow accumulation and melt.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".