Intelligent decision-support system to plan, manage and optimize water quality monitoring programs: design of a conceptual framework
Bibliographic record
Abstract
This paper presents the conceptual framework of a holistic, intelligent decision-support system (IDSS) to plan, manage and optimize water quality monitoring programs (WQMPs) for surface waters. WQMPs are a crucial component of water management because information on water quality is essential when taking action such as legislative compliance, environmental projects, urban and infrastructure development. Planning, managing and optimizing WQMPs is a complex process and requires multiple variables, rules and subject matter expert knowledge. The specific goals of this paper were to (1) assess to what extent the subject domain (WQMPs) is deemed appropriate for an IDSS; (2) describe the design process of the conceptual framework; (3) present the main elements of the system architecture; (4) present two case studies that served as potential end users; (5) illustrate the applicability of the IDSS and (6) describe subsequent steps to further test the IDSS. The IDSS was developed on the premise that the proposed system could (1) improve quality, (2) capture undocumented expertise that is perishable or in short supply (tacit knowledge), (3) provide accessible expertise to novice users, (4) have a training effect on users, and (5) show that the system, even partially complete, could still be useful. Our initial assumptions regarding these points were validated through interviews with subject matter experts. The conceptual framework was designed based on a literature review, interviews with 44 subject matter experts from Europe, Canada and the United States, interaction with end users from two case studies in the Province of Quebec, Canada, and five information technology experts from Canada and Germany. The IDSS presented in this paper will facilitate the planning, management and optimization of WQMPs. It will be exportable to various watersheds and consider the WQMP planner’s need to update the network rapidly if changes occur in human, financial and technical resources.HIGHLIGHTSHolistic and adaptable decision-support system to plan, manage and optimize surface water quality monitoring programsManagement and decision support system for knowledge acquisition processes on water qualitySystem integrates tacit and explicit knowledge on water quality monitoring challengesManagement and decision support system based on tacit knowledge from 44 experts and two case studiesAdapted design methodology for an Intelligent decision-support system Holistic and adaptable decision-support system to plan, manage and optimize surface water quality monitoring programs Management and decision support system for knowledge acquisition processes on water quality System integrates tacit and explicit knowledge on water quality monitoring challenges Management and decision support system based on tacit knowledge from 44 experts and two case studies Adapted design methodology for an Intelligent decision-support 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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".