GENERIC MODELING FRAMEWORK FOR INTEGRATED WATER RESOURCES MANAGEMENT
Bibliographic record
Abstract
By definition, integrated water resources management (IWRM) deals with planning, design and operation of complex systems in order to control the quantity, quality, temporal and spatial distribution of water with the main objective of meeting human and ecological needs and providing protection from water disasters. Complexity is a result of water resources system structure and interaction between system components. This paper presents a generic modeling framework which integrates: (i) geographic information system (GIS); (ii) system dynamics simulation model (SDS); (iii) agent-based model (ABM); and (iv) a set of physical models (hydrologic, hydraulic, water quality, reservoir operation, and river hydrodynamic). Selection of tools is driven by their ability to (a) respond to the main requirements of the IWRM; (b) properly capture the system structure; and (c) explicitly present the system behaviour as function of time and location in space. System dynamics simulation captures temporal dynamics in an integrated feedback model that includes physical and socioeconomic sectors. Management policies established in the participatory decision making environment are easily investigated through the simulation of system behaviour. Agent-based model is used to analyze spatial dynamics of complex physical-social-economic-biologic system. The IWRM modelling framework is tested using data from the Upper Thames River Watershed located in Southwestern Ontario, Canada, in collaboration with the Upper Thames River Conservation Authority.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".