Derivation of melt factors from glacier mass-balance records in western Canada
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Bibliographic record
Abstract
Abstract Melt factors for snow ( k s ) and ice ( k i ) were derived from specific mass-balance data and regionally interpolated daily air-temperature series at nine glaciers in the western Cordillera of Canada. Fitted k s and k i were relatively consistent across the region, with mean values (standard deviations) of 3.04 (0.38) and 4.59 (0.59) mm d −1 °C −1 , respectively. The interannual variability of melt factors was investigated for two long-term datasets. Calculated annually, snow- and ice-melt factors were relatively stable from year to year; standard deviations for snowmelt factors were 0.48 (17%) and 0.42 (18%) at Peyto and Place Glaciers, respectively, while standard deviations of ice-melt factors were 1.17 (25%) and 0.81 (14%). While fitted values of k s are comparable to those presented in previous observational and modeling studies, fitted k i are substantially and consistently lower across the region. Fitted melt factors were sensitive to the choice of lapse rate used in the air-temperature interpolation. Melt factors fitted to mass-balance data from a single site (Place Glacier) provided reasonable summer balance predictions at most other sites representing both maritime and continental climates, although there was a tendency for under-prediction at several sites. The combination of regionally interpolated air temperatures and a degree-day model appears capable of generating first-order estimates of regional summer balance, which can provide a benchmark against which to judge the predictive ability of more complex (e.g. energy balance) models applied at a regional scale. Mass-balance sensitivity analyses indicate that a temperature increase of 1 K will increase summer ablation in the region by 0.51 m w.e. a −1 on average.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it