The Cold Regions Hydrological Model, a Platform for Basing Process Representation and Model Structure on Physical Evidence
Bibliographic record
Abstract
After a programme of integrated field and modelling research, hydrological processes of considerable uncertainty such as snow redistribution by wind, snow interception, sublimation, snowmelt, infiltration into frozen soils, hillslope water movement over permafrost, actual evaporation, and radiation exchange to complex surfaces have been described using physically based algorithms. The Cold Regions Hydrological Model platform (CRHM), a flexible object-oriented modelling system was devised to incorporate these algorithms and others and to connect them for purposes of simulating the cold regions hydrological cycle over small to medium sized basins. Landscape elements in CRHM can be linked episodically in process-specific cascades via blowing snow transport, overland flow, organic layer subsurface flow, mineral interflow, groundwater flow, and streamflow. CRHM has a simple user interface but no provision for calibration; parameters and model structure are selected based on the understanding of the hydrological system; as such the model can be used both for prediction and for diagnosis of the adequacy of hydrological understanding. The model is described and demonstrated in basins from the semi-arid prairie to boreal forest, mountain and muskeg regions of Canada where traditional
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".