Hydrometeorological relationships on Haig Glacier, Alberta, Canada
Bibliographic record
Abstract
Abstract We investigate the relationships between meteorological, hydrological and glaciological data collected at Haig Glacier, Alberta, Canada, for the 2002 and 2003 ablation seasons. Correlation, lag cross-correlation and multivariate regression analyses are employed to assess the seasonal evolution of relationships between temperature, temperature residuals, total daily radiation, albedo, accumulation-area ratio (AAR) and total daily discharge (Qi). Early-season melt is temperature-dependent, when AAR remains high and the hydraulic properties of the snowpack limit both diurnal discharge variability and a rapid hydrologic response. As the melt season progresses, a decreasing AAR and ripening of the snowpack induce a glacier-wide decrease in albedo, and a structured radiation–discharge response is observed. Radiation-detrended temperature values offer modest improvements over physical temperature values in multivariate regression models estimating daily discharge values. Using a detrended-temperature indexed melt model, we assess the transport efficiency of the glacial hydrologic system through a comparison of total modelled daily melt and observed discharge. Transport efficiency values support the notion of a purge effect during freezing events and at the end of the ablation season, and suggest that it is the evolution of the supraglacial drainage system that controls diurnal discharge variability.
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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 | 0.003 |
| 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) | 0.002 | 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".