Bibliographic record
Abstract
Based on previous research results on river re-distribution models, a modification on the effects of topographic slopes for a runoff parameterization was proposed and implemented to the NCAR＇s land sur face model (LSM). This modification has two aspects: firstly, the topographic slopes cause outflows from higher topography and inflows into the lower topography points; secondly, topographic slopes also cause decrease of infiltration at higher topography and increases of infiltration at lower topography. Then changes in infiltration result in changes in soil moisture, surface fluxes and then in surface temperature, and eventual ly in the upper atmosphere and the climate. This mechanism is very clearly demonstrated in the point bud gets analysis at the Andes Mountains vicinities. Analysis from a regional scale perspective in the Mackenzie GEWEX Study (MAGS) area, the focus of the ongoing Canadian GEWEX program, shows that the modi fied runoff parameterization does bring significant changes in the regional surface climate. More important ly, detailed analysis from a global perspective shows many encouraging improvements introduced by the modified LSM over the original model in simulating basic atmospheric climate properties such as thermodynamic features (temperature and humidity). All of these improvements in the atmospheric climate simulation illustrate that the inclusion of topographic effects in the LSM can force the AGCM to produce a more realistic model climate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".