A Comparison of the Mosaic and Aggregated Canopy Frameworks for Representing Surface Heterogeneity in the Canadian Boreal Forest Using CLASS: A Soil Perspective.
Bibliographic record
Abstract
Abstract The Canadian Land Surface Scheme is employed in off-line tests to represent a 1200 km2 patch of the Boreal Ecosystem-Atmosphere Study Northern Study Area, a heterogeneous region of boreal forest in north-central Manitoba. The soils in the region vary from rapidly draining coarse sand to poorly drained areas of peat overlying clay, including wetlands. Observed surface fluxes from four sites, a young jack pine stand, an old jack pine stand, an old black spruce stand and a FEN, are scaled by proportional weighting to represent the study area. The performance of the mosaic approach, in which the region has been represented by four distinct patches, is compared with that of the aggregated approach, in which mean surface parameters have been defined based on fractional coverage. Various soil columns are employed in the aggregated approach, in order to assess the effects of representing the heterogeneous soils of the region in a single column. While the conservative water use at all of the sites limits the effects of changes in the soil column on the turbulent fluxes, important effects are noted. Soil evaporation is overestimated when an organic or composite soil layer is represented at the surface, and underestimated when a sandy soil column is represented. Soil evaporation can be calibrated to match that of the mosaic method, however, the need for more robust methods of accomplishing this is acknowledged. The process of aggregating the soil column eliminates the dry sandy zones which experience periods of moisture stress. This results in periods of increased canopy conductance and an overestimation of the latent heat flux. A 2-patch mosaic, with one patch consisting of sandy soil and the other patch of organic soil over clay, allows moisture stress to occur and produces modelled half-hourly sensible and latent heat fluxes that agree more closely to those of the 4-patch mosaic than any of the aggregated runs, across the range of hydro-meteorological variables. Throughout the course of the model runs presented here, the 2-patch mosaic also exhibits cumulative drainage and evapotranspiration closer to that of the 4-patch mosaic than any of the aggregated soil columns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".