Rate my Hydrograph: Evaluating the Conformity of Expert Judgment and Quantitative Metrics
Bibliographic record
Abstract
As hydrologists, we pride ourselves on being able to identify deficiencies of a hydrologic model by looking at its runoff simulations. Generally, one of the first questions that a practicing hydrologist always asks when presented with a new model is: "show me some hydrographs!". Everyone has an intuition about how a "real" (i.e., observed) hydrograph should behave [1, 2]. Although there exists a large suite of summary metrics that measure differences between simulated and observed hydrographs, those metrics do not always fully account for our professional intuition about what constitutes an adequate hydrological prediction (perhaps because metrics typically aggregate over many aspects of model performance). To us, this suggests that either (a) there is potential to improve existing metrics to conform better with expert intuition, or (b) our expert intuition is overvalued and we should focus more on metrics, or (c) a bit of both. In the social study proposed here, we aim to address this issue in a data-driven fashion: We will ask experts to access a website where they are tasked to compare two unlabeled hydrographs (at the same time) against an observed hydrograph, and to decide which of the unlabeled ones they think matches the observations better. Together with information about the experts’ background expertise, the collected responses should help paint a more nuanced picture of the aspects of hydrograph behavior that different members of the community consider important. This should provide valuable information that may enable us to derive new (and hopefully better) model performance metrics in a data-driven fashion directly from human ratings. [1] Crochemore, Louise, et al. "Comparing expert judgement and numerical criteria for hydrograph evaluation." Hydrological sciences journal 60.3 (2015): 402-423. [2] Wesemann, Johannes, et al. "Man vs. Machine: An interactive poll to evaluate hydrological model performance of a manual and an automatic calibration." EGU General Assembly Conference Abstracts. 2017.
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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 | 0.091 | 0.426 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".