The cold regions hydrological model: a platform for basing process representation and model structure on physical evidence
Why this work is in the frame
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Bibliographic record
Abstract
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 (CRHM) platform, 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 hydrological models have great difficulty in describing hydrological phenomena. Some success is shown in simulating various elements of the hydrological cycle without calibration; this is encouraging for predicting hydrology in ungauged basins. Copyright © 2007 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it