Fuzzy Criteria for Water Resources Systems Performance Evaluation
Bibliographic record
Abstract
Abstract One of the main goals of engineering design is to ensure that a system performs satisfactorily under a wide range of possible future conditions. This premise is particularly true of large and complex water resources systems. Water resources systems usually include conveyance facilities such as pipes and pumps, treatment facilities such as sedimentation tanks and filters, and storage facilities such as reservoirs and tanks. These elements are interconnected in complicated networks serving broad geographical regions. Each element is vulnerable to temporary disruption in service due to natural hazards or human error whether unintentional, as in the case of operational errors and mistakes, or due to intentional causes such as a terrorist act. Most of the hazards cannot be controlled or predicted with an acceptable degree of accuracy. Uncontrollable external factors also affect the capacity and the performance of water resource systems. The determination of the load pattern presents unique challenges. Ang and Tang (1) point out that there is uncertainty in all engineering‐based systems because these systems rely on the modeling of physical phenomena that are either inherently random or difficult to model with a high degree of accuracy.
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.
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) | 0.001 | 0.001 |
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".