Prediction of spatially distributed regional‐scale fields of air temperature and vapor pressure over mountain glaciers
Bibliographic record
Abstract
Physically based models of glacier melt require fields of near‐surface air temperature (Tg) and vapor pressure (eg) for estimating turbulent heat exchanges. However, katabatic boundary layer (KBL) processes limit the effectiveness of standard interpolation or extrapolation routines for estimating Tg and eg from regional weather station networks. Climate data collected from nine automatic weather stations operated over three ablation seasons at three glaciers in the southern Coast Mountains of British Columbia are analyzed in this study. On‐glacier observations were compared to ambient values (Ta and ea) estimated from a regional network of off‐glacier weather stations. Piecewise regressions of Tg versus Ta at each AWS site reveal (1) a critical threshold temperature (T*) that denotes the onset of katabatic boundary layer (KBL) development and (2) a temperature damping that is consistent at each site, but variable between sites. Variations in near‐surface vapor pressure are related to processes of condensation or evaporation/sublimation at the glacier surface, which are controlled by the vapor pressure gradient between the surface and the ambient air. Statistical relations with flow path lengths calculated from glacier digital elevation models are used to predict the strength of KBL effects on Tg and eg, and examples of the approach for generating distributed fields of Tg and eg are given.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".