A Lagrangian approach to modelling stable isotopes in precipitation over mountainous terrain
Bibliographic record
Abstract
Abstract A Lagrangian (Rayleigh) distillation model is used to track the evolution of stable isotopes in precipitation over mountainous terrain from the Pacific Coast of Canada to two alpine field sites in the Canadian Rocky Mountains. Precipitation δ18O at Vancouver constrains the model and air–mass back trajectories provide the water vapour pathway for 10 winter storm events. Isotopic values along storm pathways are modelled with a classical Rayleigh model that prescribes a linear decrease in temperature and pressure from initial to final conditions, and two models that account directly for orographic precipitation processes by: (i) applying an orographic rainfall model and (ii) using North American Regional Reanalysis data to calculate the change in vapour content along storm pathways. All models are significant predictors of snowpack δ18O, but the orographic model provides the best fit to precipitation‐weighted δ18O for each storm. The improvement in modelled δ18O by accounting for terrain along storm trajectories illustrates the need to account for orographically driven moisture loss when modelling vapour transport to ice core sites with mountainous upwind terrain. This finding is also applicable to isotopic studies of paleoaltimetry and source areas of groundwater recharge. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".