Determination of melt-onset and refreeze timing on southeast Alaskan icefields using SSM/I diurnal amplitude variations
Bibliographic record
Abstract
Abstract Timing of snowmelt and freeze-up was estimated for glaciers in the Coast and St Elias Ranges of Alaska, U.S.A., and British Columbia, Canada, using twice-daily brightness temperatures (Tb) from the U.S. Defense Meteorological Satellite Programs Special Sensor Microwave/Imager (SSM/I). Melt and freeze-up were determined for a 37 GHz vertically polarized time series using changes in the average daily Tb and high-amplitude Tb diurnal amplitude variations (DAV). DAV are the running difference between the early-morning (usually minimum) and late-afternoon (usually maximum) Tb observations. Year-round temperatures taken at 2 hour intervals on the Juneau Icefield (58°4N, 134°15 W) validated the microwave response to melt. A bimodal distribution of Tb corresponding to frozen or melting snow helped estimate the Tb at which the transition from frozen to melting snow occurred on pixels without ground observations. Thresholds of Tb (>246 K) and DAV (>±10 K) were used to refine the selection of melt and refreeze timing for southeast Alaska. Melt timing correlates with stream discharge. In general, melt onset occurred progressively earlier and refreeze later in the season between 1988 and 1998. It is not known whether this is related to regional warming or to one of the shorter decadal-scale oscillations in the Gulf of Alaska.
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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) | 0.000 | 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 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".