Revisiting bulk heat transfer on Peyto Glacier, Alberta, Canada, in light of the OG parameterization
Bibliographic record
Abstract
Abstract A scheme for katabatic turbulent heat transfer proposed by Oerlemans and Grisogono (2002), here referred to as the OG parameterization, is compared with bulk heat-transfer estimates on Peyto Glacier, Alberta, Canada. Automatic weather stations (AWSs) provide off-glacier data to drive the parameterization and glacier data for bulk estimates. Micrometeorological datasets are used to assess two schemes that employ the Monin-Obukhov stability parameter, z/L, to modify logarithmic, or neutral, bulk heat-transfer equations to allow for stability. Both schemes fail at >1 m above the surface, where the AWS sensors are located, unless a modified approach is used in which the stability correction is constant for z/L ≥1/3. Then the bulk sensible-heat-flux density falls to ≈0.93 of its neutral estimate at all measurement levels, thus providing a basis for comparison with the parameterization. The results of the comparison are very good, indicating that a one-to-one relationship between bulk and parameterized values can be achieved by optimizing the fit with a background exchange coefficient and, because there is only one off-glacier AWS, using a sinusoidal function to model the diurnal variation of the potential temperature lapse rate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".