Bibliographic record
Abstract
There is a strong global research effort in coupling atmospheric and hydrological models for improved hydrological flow modelling and improved atmospheric simulation. The land surface is an important hydrological control as it is the primary influence in the surface-water budget, and it is almost always a requirement in the implementation of either hydrological or atmospheric models. Sophisticated soil-vegetation atmospheric transfer schemes also known as landsurface schemes (LSS) are currently being implemented in global climate models, regional climate models and day-to-day operational forecasting numerical weather prediction models. Rarely have these been incorporated into hydrological models. Over the last 10 years, there has been a systematic attempt, through collaborative research in Canada and under a variety of research programs, to couple atmospheric and hydrological models using the LSS as the common link. Our approach has been to combine LSS with hydrological streamflow models to provide stand-alone hydrology-land-surface schemes (H–LSS). These stand-alone models are also incorporated as the LSS in the atmospheric models, creating a fully coupled system. The ability and flexibility of this system permits the analysis of sensitivities of H-LSS to parameterization and physical conceptualizations, and the models impact on hydrological and atmospheric prediction. 119 Prediction in Ungauged Basins: Approaches for Canada’s Cold Regions
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".