Derivation of melt factors from glacier mass-balance records in western Canada
Bibliographic record
Abstract
Abstract Melt factors for snow (ks) and ice (ki) were derived from specific mass-balance data and regionally interpolated daily air-temperature series at nine glaciers in the western Cordillera of Canada. Fitted ks and ki 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 ks are comparable to those presented in previous observational and modeling studies, fitted ki 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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".