Regionalization of IHACRES Model Parameters for In-tegrated Assessment across the Lake Erie, northern Ohio USA basin
Bibliographic record
Abstract
Abstract: The IHACRES model is being applied in a regionalization approach to develop streamflow predic-tions within the region of Northern Ohio, U.S.A. that drains into Lake Erie, located on the border between the U.S. and Canada. The approach to-date is based on independent univariate regressions of model parameters on watershed attributes for a collection of 11 watersheds. Anderson et al. (2005) used one of these regression relationships to represent possible effects of declining forest cover on streamflow, but did not obtain regional models for the parameters of the routing model of IHACRES. Here we apply and “validate ” a regionaliza-tion approach to estimating the full set of parameters of the IHACRES hydrologic model for integrated as-sessment across the Lake Erie, northern Ohio USA basin. We also propose that future research should focus on (1) increasing the quality of rainfall estimates as an important way to potentially improve simulation per-formance; (2) developing joint probability distributions over the full set of IHACRES model parameters to improve estimates of predictive uncertainty; and (3) developing estimates of actual forest cover trends to ob-tain more useful predictions of future trends in streamflow.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".