Validation and heterogeneity investigation of the Canadian Land Surface Scheme (CLASS) for wetland landscapes
Bibliographic record
Abstract
This thesis examines the development and validation of Canadian Land Surface Scheme (CLASS) for various wetland landscapes individually, along with an evaluation of modelled results over a heterogeneous surface with airborne observations. A further statistical analysis of the effects of land surface classification procedures over the study area and their influence on modelled results is performed. CLASS is tested over individual wetland types: bog, fen and marsh in a stand-alone (non-GCM coupled) mode. Atmospheric conditions are provided for the eight site locations from tower measured data, while each surface is parameterized within the model from site specific measurements. Resulting model turbulent and radiative flux output is then statistically evaluated against observed tower data. Findings show that while CLASS models vascular dominated wetland areas (fen and marsh) quite well, non-vascular wetlands (bogs) are poorly represented, even with improved soil descriptions. At times when the water table is close to the surface, evaporation is greatly overestimated, whereas lowered water tables generate a vastly underestimated latent heat flux. Because CLASS does not include a moisture transfer scheme applicable for non-vascular vegetation, the description of this vegetation type as either a vascular plant or bare soil appears inappropriate. CLASS was then tuned for a specific bog location found in the Hudson Bay Lowland (HBL) during the Northern Wetlands Study (NOWES). With bog surfaces better described within the model, testing of CLASS over a highly heterogeneous 169 km2 HBL region is then undertaken. The model is first modified for lake and pond surfaces and then separate runs for bog, fen, lake and tree/shrub categories is undertaken. Using a GIS, the test region under which airborne flux measurements are available is divided into 104 grid cells and proportions of each surface type are calculated within each cell. Findings indicate that although the modelled grid average radiation and flux values are reasonably well reproduced (4% error for net radiation, 10% for latent heat flux and 30% for sensible heat flux), spatial agreement between modelled and observed grid cells is disappointing. (Abstract shortened by UMI.)
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".