Bibliographic record
Abstract
Eric F. Wood was a pioneer in large-domain hydrologic modeling. Building on his work on hydrologic scaling in the 1980s, in the 1990s and 2000s Eric led the community in process-based approaches to hydrologic modeling across large geographical domains. Together with Dennis Lettenmaier, Eric developed the open-source Variable Infiltration Capacity (VIC), which became a leading large-domain hydrologic model used by dozens of research groups around the world. The capabilities of VIC advanced by Eric and Dennis' students and postdocs included improved representation of hydrologic scaling relationships, advanced representation of cold region hydrologic processes, new capabilities for large-domain streamflow forecasting, and understanding the sensitivity of large river basins to climate variability and change. Eric's work in leading community model inter-comparison projects (PILPS) and community large-domain modeling studies (GEWEX/GCIP and GEWEX/GAPP) advanced understanding of the limitations of large-domain hydrologic models and helped identify effective strategies for model improvement. It is clear that most large-domain hydrologic models that are in use today are heavily influenced by the legacy of VIC. As the community continues to advance in developing interdisciplinary approaches to Earth System modeling (integrating advances from terrestrial and aquatic ecology and the social sciences), explicitly representing a broader range of natural and human processes, it is increasingly clear that the community is indebted to the contributions of Eric F. Wood.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".