Modeling Ablation on Place Glacier, British Columbia, from Glacier and Off-glacier Data Sets
Bibliographic record
Abstract
The results of two simulations of hourly ablation, from late July to September 2002, at a site on the Place Glacier are described. First, ablation is modeled from a data set collected at the glacier site; second, from a data set collected off-glacier at a site below the glacier tongue. The glacier data set simulations, based as they are on global and reflected short-wave radiation measurements, a net long-wave radiation model and the bulk turbulent heat transfer approach, provides a reasonably good simulation of cumulative ablation, amounting to almost 1.2 m during the experimental period, mostly due to melting out of the preceding winter snowpack. Average melt component flux densities due to net short-wave radiation, net long-wave radiation, sensible heat and latent heat due to moisture exchange are 93.1, −22.6, 14.4 and 3.91 W m−2, respectively, during this time. The glacier site data are also used to fit a snow albedo model to albedo measurements for old and new snow cover. The albedo model is then used in the second simulation, which is based on global radiation measurements, a similar net long-wave radiation model, and a heat transfer approach in which turbulent mixing due to katabatic and geostrophic flow is parameterized from the off-glacier temperature data. The second simulation scheme performs best if the katabatic component of the parameterization scheme is suppressed because the wind regime at the glacier site appears to be intermittent, but the scheme itself is based upon the idea of a continuously flowing glacier wind.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".