Discrete principal‐monotonicity inference for hydro‐system analysis under irregular nonlinearities, data uncertainties, and multivariate dependencies. Part II: Application to streamflow simulation in the Xingshan Watershed, China
Bibliographic record
Abstract
Abstract Provision of reliable scientific support to socio‐economic development and eco‐environmental conservation is challenged by complexities of irregular nonlinearities, data uncertainties, and multivariate dependencies of hydrological systems in the Three Gorges Reservoir ( TGR ) region, China. Among them, the irregular nonlinearities mainly represent unreliability of regular functions for robust simulation of highly complicated relationships between variables. Based on the proposed discrete principal‐monotonicity inference ( DPMI ) approach, streamflow generation in the Xingshan Watershed, a representative watershed in this region, is examined. Based on system characterization, predictor identification, and streamflow distribution transformation, DPMI parameters are calibrated through a two‐stage strategy. Results indicate that the modelling efficiency of DPMI is satisfactory for streamflow simulation under these complexities. The distribution transformation method and the two‐stage calibration strategy can deal with non‐normality of streamflow and temporally unstable accuracy of hydrological models, respectively. The DPMI process and results reveal that both streamflow uncertainty and its rising tendency increase with flow levels. The dominant driving forces of streamflow generation are daily lowest temperature and daily cumulative precipitation in consideration of performances in global and local scales. The temporal heterogeneity of local significances to streamflow is insignificant for meteorological conditions. There is significant nonlinearity between meteorological conditions and streamflow and dependencies among meteorological conditions. The generation mechanism of low flows is more complicated than medium flows and high flows. The DPMI approach can facilitate improving robustness of hydro‐system analysis studies in the Xingshan Watershed or the TGR region. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".