The sensitivity of simulated competition between different plant functional types to subgrid‐scale representation of vegetation in a land surface model
Bibliographic record
Abstract
Abstract The Canadian Land Surface Scheme coupled to the Canadian Terrestrial Ecosystem Model is used to simulate competition between the model's seven non‐crop plant functional types (PFTs) for available space. Our objective is to assess if the model is successfully able to reproduce the observed mix of PFTs and their fractional coverages and to what extent the simulated competition is affected by the manner in which the subgrid‐scale variability of vegetation is represented. The model can be run either in a composite (single tile) configuration, where structural vegetation attributes of PFTs are aggregated for use in grid‐averaged energy and water balance calculations, or a mosaic (multiple tiles) configuration, where separate energy and water balance calculations are performed for each PFT. The model realistically simulates the fractional coverages of trees, grasses, and bare ground, as well as that of individual tree and grass PFTs and their succession patterns. Our results show that the model is not overly sensitive to the manner in which subgrid‐scale variability of vegetation is represented. Of the seven sites chosen across the globe to illustrate the difference between the two configurations, the simulated fractional coverage of PFTs are generally very similar (root‐mean‐square difference, RMSD, < 5%) between the composite and mosaic configurations at locations characterized by low heterogeneity (e.g., Amazonia, Vancouver Island, and the Tibetan Plateau), whereas at locations characterized by high heterogeneity (e.g., India, South Sudan and California), the two configurations yield somewhat different results (RMSD > 5%).
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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 | 0.001 | 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) | 0.000 | 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 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".