Translating hydrology research into practice: A Canadian Perspective
Bibliographic record
Abstract
Hydrology research is regarded as vital for advancing human development and environmental conservation through improved hydrological process understanding and by devising solutions to address water management challenges. This is particularly acute in a time of global change and the need to find pathways to water sustainability. Success for research in hydrology is often measured through quantitative research outputs, such as the number of journal publications, citation indices, number of students trained, patents, and external research funding. User involvement in the research and development process is rarely considered a metric for success in hydrology. Despite successful scientific or engineering advancements, a greater scientific understanding of hydrology and ever-increasing publications, much of the research has limited uptake by practitioners and implementation into practice, leading to a growing gap between research and practice. This lack of utilisation is not due to a lack of need by users, but rather is a symptom of the disconnect between these advances and research that would most add value to practitioners and their application needs. We explore some outstanding challenges in translating academic research into practice and make some recommendations to bridge the increasing gaps between research and practice through a transdisciplinary approach, user engagement metrics in funded research and strong knowledge mobilization. We also discuss the success and challenges of these approaches in the Global Water Futures program along with lessons learned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.026 | 0.049 |
| Scholarly communication | 0.039 | 0.019 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".