Water resources management – towards performance based approach.
Bibliographic record
Abstract
Global change that results from population growth, global warming, and land use change (especially rapid urbanization) directly affects the complexity of water resources management problems and the uncertainty they are exposed to. Both, the complexity and the uncertainty, are the result of dynamic interactions of innumerable system parts within three major systems: (i) the physical environment; (ii) the social and demographic characteristics of the region under consideration; and (iii) the pipes, roads, bridges, buildings, and other components of the constructed environment (infrastructure). Recent trends in dealing with complex water resources systems include consideration of the entire region being affected, explicit consideration of all costs and benefits, elaboration of a large number of alternative solutions, and the greater participation of all stakeholders in the decision-making. Systems approaches based on simulation, optimization, and multi-objective analyses, in deterministic, stochastic and fuzzy forms, demonstrated in the last 50 years, an excellent potential for providing appropriate support for effective water resources management. This paper explores the future opportunities based on the advances in systems theory that can, on a broader scale, majorly transform the management of water resources. The paper identifies performance-based water resources engineering as a methodological framework to improve water resources management in the face of rapid climate destabilization so that sustainability becomes the norm, not the occasional success story.
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 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".