Spatial and temporal variability of freshwater discharge into the <scp>G</scp>ulf of <scp>A</scp>laska
Bibliographic record
Abstract
Abstract A study of the freshwater discharge into the Gulf of Alaska (GOA) has been carried out. Using available streamgage data, regression equations were developed for monthly flows. These equations express discharge as a function of basin physical characteristics such as area, mean elevation, and land cover, and of basin meteorological characteristics such as temperature, precipitation, and accumulated water year precipitation. To provide the necessary input meteorological data, temperature and precipitation data for a 40 year hind‐cast period were developed on high‐spatial‐resolution grids using weather station data, PRISM climatologies, and statistical downscaling methods. Runoff predictions from the equations were found to agree well with observations. Once developed, the regression equations were applied to a network of delineated watersheds spanning the entire GOA drainage basin. The region was divided into a northern region, ranging from the Aleutian Chain to the Alaska/Canada border in the southeast panhandle, and a southern region, ranging from there to the Fraser River. The mean annual runoff volume into the northern GOA region was found to be 792 ± 120 km 3 yr −1 . A water balance using MODIS‐based evapotranspiration rates yielded seasonal storage volumes that were consistent with GRACE satellite‐based estimates. The GRACE data suggest that an additional 57 ± 11 km 3 yr −1 be added to the runoff from the northern region, due to glacier volume loss (GVL) in recent years. This yields a total value of 849 ± 121 km 3 yr −1 . The ease of application of the derived regression equations provides an accessible tool for quantifying mean annual values, seasonal variation, and interannual variability of runoff in any ungaged basin of interest.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| 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".