Case Studies of Canadian Environmental Decision Support Systems
Bibliographic record
Abstract
developers, modellers and Geographic Information System (GIS) specialists. The integration includes data, maps and models with user-friendly tools, including data input/output views, map input/output views, and modelling result views for interpretation, further analysis, conclusion and recommendation with the aid of the expert system approach. The second example of an EDSS is one that has been developed by Environment Canada to provide policy makers with a tool to help in examining management options for dealing with the impacts of land use on water for agricultural issues in Canada. The system deals with both temporal and spatial consistency among component models, where the output from one model is used as input to another in a sequence of linked calculations. In this example, the dynamic landscape model generates land use maps for various land use scenarios that can be used either in a single storm event non-point source pollutant model such as the Agricultural Non-Point Source Pollutant Model (AGNPS) It also provides the ability for scenario gaming, and testing, pollutant source tracing and the determination of optimal solutions. Examples will be provided of its application to a watershed in Ontario Canada. Finally we will summarize the effectiveness of these systems and some insights as to how they might be improved and future directions. Understanding complex environmental problems and making informed resource management decisions requires the integration of scientific data and knowledge across multiple disciplines and diverse landscapes. Ever increasing demands for timely, accurate and spatially explicit information require environmental modellers to deploy the latest information technology to provide decision support for various departmental priorities, such as global climate change, point source and non-point source pollution, lake eutrophication, biodiversity and ecosystem sustainability. Decision Support Systems (DSS) (Alter, 1980) are computer-based interactive humancomputer decision-making systems that assist policy makers in decision making processes. These systems utilize data and models to solve domain-specific problems and focus on effectiveness rather than efficiency in decision making processes. They also make informed resource management decisions and require the integration of scientific data, information, models and knowledge across multi-media (air, land and water), multi-disciplines and diverse landscapes in better understanding complex environmental issues. In order for any decision support system to be a success, a proper design process is critical. The design of any environmental decision support system (EDSS) should come from a diversified functional group. They are scientists, environmental modellers, decision support system developers, computer programmers and component specialists such as Geographic Information Systems. Each of them contributes certain aspects of the system and how all the pieces fit into the system seamlessly. The system's blueprint should come from scientists who understand what is most required. Figure At a glance, one can see that this kind of system offers a generic framework to integrate data, text, maps, objects, images, videos, environmental models and knowledge with userfriendly tools, including database management systems, mapping systems, visualization, advanced statistics, analytical functions and expert systems/artificial intelligence tools to produce outputs for interpretation, integration, post analysis and recommendation. An EDSS should be able to handle data rich and poor situations. It should be functionality rich so the EDSS users not only perform the existing analytical routines but also expand to incorporate new ideas. Thus, a good EDSS should consist of the following functions and features:
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".