Peeking Inside Hydrologists' Minds: Comparing Human Judgment and Quantitative Metrics of Hydrographs
Bibliographic record
Abstract
Everyone wants their hydrologic models to be as good as possible. But how do we know if a model is accurate or not? In the spirit of rigorous and reproducible science, the answer should be: we calculate metrics. Yet, as humans, we sometimes follow a scheme of "I know a good model when I see it" and manually inspect hydrographs to assess their quality. This is certainly a valid method for sanity checks, but it is unclear whether these subjective visual ratings agree with metric-based rankings. Moreover, the consistency of such inspections is unclear, as different observers might come to different conclusions about the same hydrographs.In this presentation, we report a large-scale study where we collected responses from 622 experts, who compared and judged more than 14,000 pairs of hydrographs from 13 different models. Our results show that overall, human ratings broadly agree with quantitative metrics in a clear preference for a Machine Learning model. At the level of individuals, however, there is a large amount of inconsistency between ratings from different participants. Still, in cases where experts agree, we can predict their most likely rating purely from qualitative metrics. This indicates that we can encode intersubjective human preferences with a small set of objective, quantitative metrics. To us, these results make a compelling case for the community to put more trust into existing metrics—for example, by conducting more rigorous benchmarking efforts.
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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 | 0.042 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".