How to strengthen interdisciplinarity in ecohydraulics? Outcomes from ISE 2018
Bibliographic record
Abstract
Interdisciplinary approaches are required to tackle complex environmental issues as freshwater ecosystems face unprecedented pressures globally. The emerging Ecohydraulics field of research should, therefore, take steps towards developing true interdisciplinarity to adapt to a continuing changing world. This study contributes to the ongoing discussion on interdisciplinarity in Ecohydraulics and shapes its growth by identifying key actions, actors and implementation strategies that can strengthen it. Based on an online questionnaire and a workshop involving over 150 early and established careers, we present a list of 20 prioritised actions that will help engage the research community towards specific goals and will result in increased interdisciplinary outcomes. While early career researchers (ECRs) have taken the lead on creating this roadmap, its implementation should be a joint responsibility of both ECRs, established career scientists, groups, and institutions within Ecohydraulics. The list of identified actions and assigned responsibility should, therefore, be considered a conversation starter. Continued revision of the here-stated approaches will be required in the future as the field of research and its community progresses. With this contribution, we resume a critical reflection on where the Ecohydraulics field of research and community stand today and suggest where resources should be invested in the long-term to consolidate the inherent interdisciplinarity in Ecohydraulics.
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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 | 0.027 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".