Post-depositional modification of stable water isotopes in winter snowpacks in the Canadian Rocky Mountains
Bibliographic record
Abstract
Abstract To assess the seasonal stability of the δ18O stratigraphy in winter snowpacks in the Canadian Rocky Mountains, snow pits were sampled over three accumulation seasons at two field sites. These sites, Opabin and Haig Glaciers, are ~160km apart at similar elevations and represent windward and lee-slope environments respectively. At both sites, snow pits were sampled at one glacier and one forefield location throughout each accumulation season. Intra-seasonal changes in δ18O at each site were examined to determine the extent of post-depositional modification of isotope stratigraphies. At both glacier sites, there was minimal temporal change before the onset of spring melt in all years. In addition, the similar structure of δ18O profiles from both glacier sites suggests that regional controls govern the isotopic composition of solid-phase precipitation across the study area. At forefield locations, the absence of an insulating layer of ice at the base of the snowpack allowed for vapour transport and post-depositional modification of the seasonal δ18O signal. This did not result in consistent changes to the mean δ18O, deuterium excess and δD–δ18O regression line slopes in the lower layers of snow, and the observed smoothing of δ18O profiles was less than that simulated by applying a diffusion model to these snowpacks.
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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 | 0.000 |
| 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 | 0.001 | 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".