Increasing Importance of Local Hydroclimatology During the Tundra Growing Season in the Yukon–Kuskokwim Delta
Bibliographic record
Abstract
Changing precipitation patterns in the Arctic is a key indicator of climate change, in addition to increasing land and ocean temperatures, but these patterns are not uniform across the circumpolar region. This regional analysis focuses on the Yukon–Kuskokwim Delta in southwestern Alaska and addresses the following questions: (1) What is the baseline hydroclimatology during the growing season on the Yukon–Kuskokwim Delta? (2) What are the seasonal and intraseasonal trends of the hydroclimate variables in the YKD? (3) What are the implications of documented trends for the study region? Utilizing ECMWF’s ERA5 reanalysis dataset, we conducted a seasonal analysis for May through September for the years 1982–2022. While no strong trend emerged for total precipitation over the 41-year study period, differing trends were observed for large-scale and convective precipitation. The decline in large-scale precipitation is supported by a decrease in storm counts in the Bering Sea, as well as declining vertically integrated moisture convergence and moisture flux. By contrast, the increase in convective precipitation underscores the growing importance of the local hydrologic cycle, further supported by a significant rise in evaporation. These enhanced local hydroclimatological cycles have significant implications for wildfires and subsistence activities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".