Patterns, puzzles and people: implementing hydrologic synthesis
Bibliographic record
Abstract
Abstract There have been several calls made for hydrologic synthesis research: namely activities which unify diverse data sources across sites, scales and disciplines to uncover new connections and to promote a holistic understanding of water science. This paper draws on the NSF‐funded Hydrological Synthesis Project (HSP) run by the University of Illinois at Urbana‐Champaign to elucidate mechanisms, benefits and challenges of implementing hydrologic synthesis research from the perspectives of participants in a pilot research study. Two broadly different mechanisms of implementing synthesis were adopted in the HSP: 6‐week Summer Institutes in which Ph.D. students conducted team‐based research under the guidance of faculty mentors, and focused workshops which disseminated knowledge and shared experiences between scientists at many different career levels. The Summer Institutes were a test bed in which new ideas could be explored, assisted students in developing a wide range of skills, and were highly productive, but posed challenges for mentors and students because the ‘new’ research topics initiated during the Institutes' programmes needed to be completed in competition with students' ongoing Ph.D. research or mentor's existing research programs. The workshop‐based model circumvented this conflict and was also highly productive, but did not offer the same opportunity to experiment with new ideas as part of the synthesis research. Leadership, trust, flexibility and long gestation times were all important to bringing synthesis research to a positive resolution. Funding models that embrace the exploratory aspects of synthesis and provide adequate support to mentors and students over these long timescales would facilitate future hydrologic synthesis research. Copyright © 2011 John Wiley & Sons, Ltd.
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 | 0.066 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".