Comparing the performance of the Canadian land surface scheme @class) for two subarctic terrain types
Bibliographic record
Abstract
The Canadian Land Surface Scheme (CLASS) is tested for two major terrain types found in the northern Hudson Bay Lowland. Soil temperature and energy balance measurements for sedge fen wetland and dwarf willow‐birch forest near Churchill, Manitoba, are compared with simulations both with and without the new organic soil parametrization that has been developed for CLASS (Letts et al., 2000), for eight datasets spanning six years (1990 through 1995). With the exception of the sensible heat flux at the sedge site, the new version of CLASS with the organic soil parametrization improves the energy budget simulation at each of the research sites. Both the latent (QE) and ground (QG) heat flux were modelled well; however, some modifications were required to simulate the continued evaporation from these sites once the water table receded below the first soil layer. The sensible heat flux (QH) was the least well simulated component of the energy balance in both versions. Temperatures for the top two soil layers were consistently overestimated by the mineral soil parametrization for both terrain types, whereas the organic soil parametrization showed significant improvement. The new water table diagnostic algorithm in the organic soil version satisfactorily estimates the position of the water table, even under a large range of climatic conditions. The inclusion of organic soil parameters in CLASS, and the subsequent improved handling of soil moisture, is a significant contribution to model development, and provides a physically‐based capability for simulating northern peatlands within land surface models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".