Measurement and parameterization of albedo variations at Haut Glacier d’Arolla, Switzerland
Bibliographic record
Abstract
Abstract Spatial and temporal variations of surface albedo on Haut Glacier d’Arolla, Switzerland, during the 1993 and 1994 ablation seasons are described. Correlation and regression analyses are used to explain the albedo variations in terms of independent meteorological and surface property variables. Parameterizations are developed which allow estimation of albedo variation in surface energy-balance models. Snow albedo is best estimated from accumulated daily maximum temperatures since snowfall. On “deep” snow (≥0.5 cm w.e. depth) a logarithmic function is used, while on “shallow” snow (<0.5 cm w.e. depth) an exponential function is used to enable the albedo to decay to the underlying ice or debris albedo. The transition from “deep” to “shallow” snow is calculated as a function of decreasing snow depth (combined r2 = 0.65). This new parameterization performs better than earlier schemes because accumulated daily maximum temperatures since snowfall correlate strongly with snow grain-size and impurity concentration, the main physical controls on snow albedo. Ice albedo may be parameterized by its relationship to elevation (r2 = 0.28), but this approach results in only a small improvement over the assumption of a constant mean ice albedo.
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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) | 0.001 | 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".