Setting expectations for hydrologic model performance with an ensemble of simple benchmarks
Bibliographic record
Abstract
Example of benchmark inputs and results for a snow-dominated basin, subset to a 4-year period on either side of the calculation (left) and evaluation (right) divide (dotted red line). KGE scores in legends are calculated for the evaluation period. (a) Observed streamflow, precipitation and a ‘rain plus melt’ flux (RPM) derived from precipitation and temperature. RPM is used to define the benchmarks shown in c and d. (b) Flow-only benchmarks. The straight light green line is the traditional (NSE = 0; KGE = 1-√2) mean flow benchmark. (c) Rainfall-runoff ratio benchmarks. A single rainfall-runoff ratio is derived from the data in the calculation period and used to scale annual and monthly RPM sums into flow benchmarks. (d) Simple models that represent catchment function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".