Impact of precipitation on parameter sensitivity and identifiability in the variable infiltration capacity (VIC) model
Bibliographic record
Abstract
This study aims to explore the interaction between selected gridded precipitation products and parameters of the variable infiltration capacity (VIC) model within the Gharesu basin in Iran. The evaluation is done in two phases: (1) exploration of the performance of various precipitation products (i.e. three satellite gauges, two reanalyses, and three gauge-based datasets), and comparison to station measurements; and (2) the interaction of behavioural VIC parameter sets with these precipitation products. Evaluation of best-performing and non-dominant solutions indicates that for most VIC parameters, no unique value and range can be specified for the precipitation products. This reflects the important fact that precipitation, which is known to carry significant information, is decisive in inferred values of parameters that are perceived to have physical meaning (e.g. soil characteristics). In summary, our research contributes to a deeper understanding of the complex relationship between precipitation products and parameters of hydrological models.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".