Design Considerations for Watershed Management Decision Support Systems
Bibliographic record
Abstract
Abstract Increasing attention is being paid to the management of water resources on a watershed basis, necessitating a cross-disciplinary approach to data collection and analysis. Traditional assessments of water quality and quantity are being joined by assessments of biology, botany, geomorphology, and anthropological subjects such as economic valuation. This integrated problem domain calls for a reassessment of the information technology tools designed to support the management process. With a comprehensive requirements analysis pulled from a survey of water resource practitioners, the functions necessary for design of a contemporary watershed management decision support system (WMDSS) are outlined and assessed in light of current tools in use today. Following a systems engineering methodology, the WMDSS requirements are analyzed and ranked in order of priority. This yields a ranking for development of tool and information functional groups to support the following assessment types: surface water quality, surface levels and flows, integration, groundwater flows/levels, rainfall/runoff modelling and time series analysis. Functional analysis then provides the architecture and data flows necessary to meet system requirements. The WMDSS functional analysis is concluded with a recommended architecture for design of such a system. This sets the foundation for follow-on work in production and validation of the system.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".