Towards more credible models in catchment hydrology to enhance hydrological process understanding: Preface
Bibliographic record
Abstract
Catchment modelling has undergone tremendous developments during the past decades. In the 1970s, the focus was on simulation of catchment runoff with process descriptions and data inputs being lumped to the catchment scale. Later developments included spatially distributed models allowing data inputs and hydrological processes to be simulated at model grid scale, i.e. much finer than catchment scale. These models were able to explicitly simulate various processes such as soil moisture, evapotranspiration, groundwater and surface runoff. With the advancements in remote sensing technology and availability of high-resolution data, increased attention has in recent years been given to enhancing the capability of catchment models to reproduce spatial patterns and in this way improve our understanding of hydrological processes and the physical realism of catchment models. This development process has involved a wide spectrum of different aspects in the modelling process, reaching from an improved understanding of uncertainties in data, model parameters and model structures to new protocols for good modelling practices in water management. Recognizing the important role of biodiversity and social aspects, hydrologists are now extending the scope of their models to capture the interactions between water, biota and human social systems.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".