Pareto-optimality and a search for robustness: choosing solutions with desired properties in objective space and parameter space
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multi-objective genetic algorithms are increasingly being applied to calibrate hydrological models by generating several competitive solutions usually referred to as a Pareto-optimal set. The Pareto-optimal set comprises non-dominated solutions at the calibration phase but it is usually unknown whether all or only a subset of non-dominated solutions at the calibration phase remains non-dominated at the validation phase. In practice, users would like to know solutions (and their associated properties) which remain non-dominated at both the calibration and validation phases. This study investigates robustness of the Pareto-optimal set by developing a model characterization framework (MCF). The MCF uses cluster analysis to examine the distribution of solutions in parameter space and objective space, and conditional probability to combine linkages between the distributions of solutions in both spaces. The MCF has been illustrated for calibration output generated from application of the Non-dominated Sorting Genetic Algorithm-II to calibrate the Soil and Water Assessment Tool for streamflow in the Fairchild Creek watershed in southern Ontario. Our results show that not all non-dominated solutions found at the calibration phase perform the same for different validation periods. The MCF illustrates that robust solutions – non-dominated solutions which cluster in similar locations in parameter space and objective space – performed consistently well for several validation periods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it