Temperature and vapour-trajectory controls on the stable-isotope signal in Canadian Rocky Mountain snowpacks
Bibliographic record
Abstract
Abstract The effects of temperature and seasonal air-mass trajectories on stable water isotopes in alpine snowpacks are investigated using meteorological and snow-pit data at two alpine field sites in the Canadian Rocky Mountains: Haig Glacier, Alberta, and Opabin Glacier, British Columbia. Snow pits were sampled through three accumulation seasons (October–June, 2004/05, 2005/06 and 2006/07) for δ18O, δD, temperature and density. The isotopic characteristics of precipitation over these time periods, including the local meteoric waterline and average δ18O, δD and deuterium excess, were defined using this dataset. Individual snowfall events over the three seasons were identified in the accumulation records from both sites and then fit to snow-pit stratigraphies to determine their mean isotopic characteristics. A trajectory classification was produced for all events, and the key meteorological characteristics of each trajectory class were investigated using data from alpine field sites and a suite of meteorological records from the region. An analysis of the relative influences of temperature and air-mass trajectory on snow isotope ratios reveals some separation in mean δ18O between storm classes. However, the separation appears to be driven primarily by the mean temperature of each class rather then being a direct effect of vapour pathway.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".