Operational snow condition estimates in the extratropical Andes Cordillera through physically based, snowdrift permitting numerical modeling
Bibliographic record
Abstract
<!--!introduction!--> Snow processes are essential for hydrology in central Chile, as snowmelt is the most influential contribution to streamflow during the dry season. Thus, having a reliable representation of the snowpack condition in the cordillera is fundamental for stational streamflow forecasting for agriculture and hydropower generation, water management, and is useful for population in general. Previous studies have reconstructed the snowpack in central Chile in the historical period, but an updated and operational product is not available. In this study, we build an operational product of the snowpack over the Andes cordillera (27-37°S), implementing the Canadian Hydrological Model (CHM), a multi-scale, spatially distributed, modular and modeling framework, which uses conceptual and physically-based process representations. CHM is computationally efficient thanks to high performance computing protocols and to a spatial discretization through unstructured triangular meshes. This study considers multi-scale snow processes, such as blowing-snow, avalanches and canopy effects, in a large-scale application in the Andes cordillera informed by observational and remote sensing data and evaluated against intensive fieldwork campaigns. The initial implementation presented here highlights the opportunities and knowledge gaps that must be bridged in this data-scarce mountain region of the world, and is intended as a platform upon which to organize future collaborative research efforts.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".