Use of Mixed Methods in Hydrological Science: what are their Contributions?
Bibliographic record
Abstract
Research in hydrological sciences is constantly evolving to provide adequate answers to water-related issues. Methodological approaches inspired by mathematical sciences and physical sciences have shaped hydrological sciences from its beginnings to the present day. But nowadays with the increasing complexity of hydrological phenomena, hydrological sciences have integrated approaches from the social sciences which provide missing information for the study of complex hydrological objects which is the observation and perception of water resources by users. A methodological approach: the mixed methods with their different research designs make it possible to combine the quantitative approaches of the physical and mathematical sciences and the qualitative approaches of the social sciences to understand the object of study and propose adequate solutions for its management. We detail here, the use of mixed methods in research in flood hydrology, in research on low flow conditions and on the management of these hydrological extremes. Mixed methods contributions to these studies are diverse and pragmatically relevant for hydrology. They range from the densification of data on extreme flood events to reduce forecasting uncertainties, to the production of knowledge on low-flow hydrological states that are insufficiently documented and finally to support participatory management decision-making about extreme hydrological events and water management.
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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 | 0.188 | 0.302 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".