Generic Integration in Environmental Information and Decision Support Systems
Bibliographic record
Abstract
Environmental Information Systems (EIS) and Environmental Decision Support Systems (EDSS) are major building blocks in environmental management and science today. They are used at all levels of public bodies (community, state, national and international level), in science, in management and as information platforms towards the public. EIS and EDSS are usually said to have certain characteristics, which distinguish them from standard information systems, e.g. information complexity in time and space or uncompleteness or fuzzyness of data items. By the very nature of the complex tasks involved, different methodologies can be an option while developing a new system, for instance modelling, decision theoretic approaches, artificial intelligence, geographical analysis, statistics and many more. As software developers, we face the situation that we have to recompose these different methodologies in different application scenarios over and over again. This is rather cumbersome, because the tools implementing certain methodologies are usually not very helpful in the integration process. This paper discusses the question, how different EIS and EDSS tools can be integrated in a generic way. For this purpose, we discuss a number of integration strategies and give 2 examples of current EU-funded projects.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".