Seasonal and Interannual Variability of Melt-Season Albedo at Haig Glacier, Canadian Rocky Mountains
Bibliographic record
Abstract
Abstract. In situ observations of summer albedo are presented for the period 2002–2017 from Haig Glacier in the Canadian Rocky Mountains. The observations provide insight into the seasonal evolution and interannual variability of snow and ice albedo, including the effects of summer snowfall, the decay of snow albedo through the melt season, and the potential short-term impacts of regional wildfire activity on ice albedo reductions. Mean summer albedo (±1σ) recorded at an automatic weather station in the upper ablation zone of the glacier was αS = 0.55 ± 0.07 over this period, with no evidence of long-term albedo trends. Each summer the surface conditions at the weather station undergo a transition from a dry, reflective springsnowpack (αS ∼ 0.8), to a wet, homogeneous mid-summer snowpack (αS ∼ 0.5), to exposed, impurity-rich glacier ice, with ameasured albedo of 0.21 ± 0.06 over the study period. The ice albedo drops to ~ 0.1 during years of intense regional wildfire activity such as 2003 and 2017, but it recovers from this in subsequent years. Summer snowfall events have a significant influence on albedo, and a stochastic parameterization of these events is shown to improve modelled estimates of summer albedo and mass balance. Modifications to conventional degree-day melt factors are also suggested, to better capture the effects of seasonal albedo evolution in climate, hydrology, and glacier mass balance models that use temperature index or positive-degree day methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".