Tiling soil textures for terrestrial ecosystem modelling via clustering analysis: a case study with CLASS-CTEM (version 2.1)
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Bibliographic record
Abstract
Abstract. We investigate the application of clustering algorithms to represent sub-grid scale variability in soil texture for use in a global-scale terrestrial ecosystem model. Our model, the coupled Canadian Land Surface Scheme – Canadian Terrestrial Ecosystem Model (CLASS-CTEM), is typically implemented at a coarse spatial resolution (approximately 2. 8° × 2. 8°) due to its use as the land surface component of the Canadian Earth System Model (CanESM). CLASS-CTEM can, however, be run with tiling of the land surface as a means to represent sub-grid heterogeneity. We first determined that the model was sensitive to tiling of the soil textures via an idealized test case before attempting to cluster soil textures globally. To cluster a high-resolution soil texture dataset onto our coarse model grid, we use two linked algorithms – the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm (Ankerst et al., 1999; Daszykowski et al., 2002) and the algorithm of Sander et al. (2003) – to provide tiles of representative soil textures for use as CLASS-CTEM inputs. The clustering process results in, on average, about three tiles per CLASS-CTEM grid cell with most cells having four or less tiles. Results from CLASS-CTEM simulations conducted with the tiled inputs (Cluster) versus those using a simple grid-mean soil texture (Gridmean) show CLASS-CTEM, at least on a global scale, is relatively insensitive to the tiled soil textures; however, differences can be large in arid or peatland regions. The Cluster simulation has generally lower soil moisture and lower overall vegetation productivity than the Gridmean simulation except in arid regions where plant productivity increases. In these dry regions, the influence of the tiling is stronger due to the general state of vegetation moisture stress which allows a single tile, whose soil texture retains more plant-available water, to yield much higher productivity. Although the use of clustering analysis appears promising as a means to represent sub-grid heterogeneity, soil textures appear to be reasonably represented for global-scale simulations using a simple grid-mean value.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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