A MESO-β SCALE SIMULATION OF THE EFFECTS OF BOREAL FOREST ECOSYSTEM ON THE LOWER ATMOSPHERE
Bibliographic record
Abstract
Based on the Intensive Field Campaign(IFC-1)data of Boreal Ecosystem-Atmosphere Study (BOREAS).a three-dimensional meso-β scale model is used to simulate the effect of boreal forests on the lower atmosphere.A fine horizontal resolution of 2 km×2 km is used in order to distinguish the vegetative heterogeneity in the boreal region.A total of 20×25 grid points cover the entire sub- modeling area in BOREAS' South Study Area(SSA).The ecosystem types and their coverage in each grid square are extracted from the North American Land Cover Characteristics Data Base (NALCCD)generated by the U.S.Geographical Survey(USGS)and the University of Nebraska- Lincoln(UNL).The topography of the study area is taken from the Digital Elevation Map(DEM) of USGS.The model outputs include the components of the energy balance budget within the canopy and at the ground.the turbulence parameters in the atmospheric boundary layer and the wind. temperature and humidity profiles extending up to a height of 1500 m.In addition to the fine time and spatial step,the unique feature of the present model is the incorporation of both dynamic and biological effects of the Boreal forest into the model parameterization scheme.The model results compare favorably with BOREAS' IFC-1 data in 1994 when the forest was in the luxuriant growing period.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".