Longer spring snowmelt: spatial and temporal variations of snowmelt trends detected by passive microwave from 1988 to 2010 in the Yukon River Basin
Bibliographic record
Abstract
Abstract. Brightness temperature (Tb) data from the Special Sensor Microwave Imager (SSM/I) 37 V-GHz frequency provides a time series from 1988 to 2010 that enables the assessment of snowmelt timing trends (onset, end of melt-refreeze, and duration) for the Yukon River Basin. Tb and diurnal amplitude variation (DAV) thresholds determine dates of melt onset and melt-freeze end (end of high DAV), defined as the first date when thresholds are met for more than three of five consecutive days. Temporal and spatial trends in melt onset and end of melt-refreeze date are determined with varying time period intervals and for each sub-basin and elevation class. Earlier melt onset trends are found in the highest elevations and northernmost sub-basins (Porcupine, Chandalar, and Koyukuk Rivers). Significant later (>0.75 d yr−1) end of melt-refreeze and longer melt duration trends are found in a majority of the sub-basins. Moving interval trends suggest interannual variability within the time series and a power spectrum analysis reveals peak frequencies and periods of 5–7 and ~11 years, possibly related to El Nino- Southern Oscillation and the solar cycle, respectively. Latitude and elevation display the dominant controls on timing variance and spring solar flux is highly correlated with melt timing in middle elevations.
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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".