Evaluating the controls of soil moisture variability within the Canadian Land Surface Scheme (CLASS)
Bibliographic record
Abstract
Soil water availability impacts vegetation distribution and health, biogeochemical cycles, climate, groundwater recharge and streamflow. Therefore, characterization of the processes that control soil moisture variability in time and space has broad implications. The aim of this research is to evaluate the processes controlling soil moisture variability observed within an in-situ soil moisture-monitoring network and contrast these results with those obtained from a land surface parametrization scheme. The processes controlling soil moisture variability were derived using principal component analysis (PCA) of a regional scale soil water content network over Alberta, Canada. An identical PCA was computed for the Canadian Land Surface Scheme (CLASS) to identify the physical processes controlling the explained variability. In both the model and in observations, the first and second principal components can be statistically linked to drainage and evaporative processes respectively. Using this approach, differences between process controls observed in the model and in observations can be attributed to specific processes, thus facilitating future model development.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".