Bibliographic record
Abstract
Abstract. This study presents a 35-year snow phenology record for the Yukon River Basin (YRB), developed using a Random Forest (RF) model at a 3.125 km resolution, capturing detailed trends in snowmelt onset and snowoff. The RF model, incorporating dynamic daily variables, improves upon traditional threshold-based methods by reducing sensitivity to transient thaw events and atmospheric noise. Model evaluation against station observations yielded a mean absolute error (MAE) of 11.6 days and a root mean square error (RMSE) of 14.9 days for snowmelt onset. For snowoff, the model achieved a MAE of 18.1 days and an RMSE of 21.3 days. This approach successfully mapped snow phenology across the diverse YRB landscape, providing valuable insight into how variations in snow cover align with regional climate patterns. Challenges such as sample bias due to limited ground-based data coverage highlight the need for expanding in-situ measurements, to improve model performance further. Trend analysis segmented by two timeframes, 1988–2005 and 2006–2023, revealed distinct climate impacts on snow phenology. During 1988–2005, high snowfall and stable temperatures resulted in hastened snowmelt onset and lengthened snowmelt durations, reflecting early-season snow abundance. In contrast, from 2006–2023, warming spring and summer temperatures corresponded with progressively earlier snowmelt onset and snowoff. These shifts in snowmelt patterns align with a lengthened snow-free season, indicating increasing influence of warmer temperatures on the snowpack. This RF-derived dataset provides an essential tool for tracking climate-driven snow changes, offering insights into hydrologic and ecologic dynamics in the YRB under accelerating climate change.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.007 |
| Insufficient payload (model declined to judge) | 0.503 | 0.342 |
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".