Application of the Canadian land surface scheme (class) to the simulation of energy and water fluxes over alpine tundra
Bibliographic record
Abstract
Abstract The ability of the Canadian Land Surface Scheme (CLASS) to simulate energy and moisture fluxes over tundra surfaces is tested using three dataseis collected at alpine sites in southern Alberta and British Columbia, Canada. Initial runs of the model indicate that the ground heat flux tends to be overestimated and the latent heat flux underestimated on average. With the incorporation of minor modifications to the surface thermal conductivity, the vegetation rooting depth and the calculation of the surface soil moisture, the mean bias errors in the latent and ground heat fluxes are reduced to more acceptable levels. Despite the fact that the current version of CLASS does not explicitly take into account the effects of spatial heterogeneity at the sites, the model is found to perform reasonably well with these modifications. It is recommended that the next version of CLASS incorporate a mosaic approach which will allow further subdivision of the modelling area, and that a set of algorithms specific to sparse canopies be implemented into the code. Résumé La capacité du schéma CLASS (≪Canadian LAnd Surface Scheme≫) à simuler les flux d'énergie et d'humidité sur des sols de toundra est testée en utilisant trois ensembles de données recueillies à des sites alpins dans le sud de l'Alberta et de la Colombie‐Britannique, au Canada. Les passes initiales du modèle indiquent qu 'en moyenne le flux de chaleur dans le sol a une tendance à être surestimé et que le flux de chaleur latente à être sous‐estimé. Avec l'incorporation de modifications mineures à la conductivité thermique superficielle, à la profondeur d'enracinement de la végétation et au calcul de l'humidité superficielle du sol, les erreurs d'écarts moyens dans les flux de chaleur dans le sol et de chaleur latente sont réduites à des niveaux plus acceptables. Malgré le fait que la version actuelle du schéma CLASS ne tient pas compte explicitement des effets de l'hétérogénéité spatiale aux sites, on trouve que le modèle s'exécute raisonnablement bien avec ces modifications. Il est recommandé que la prochaine version du schéma CLASS incorpore une approche mosaïque qui permettra une subdivision addionnelle de la surface modélisée; on recommande aussi qu'un ensemble d'algorithmes spécifique à des couverts végétaux épars, soit incorporé au code. Notes Corresponding author: diana.verseghy@ec.gc.ca
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 teacher head, 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".