Fuzzy logic based IEDSSs for environmental risk assessment and management
Bibliographic record
Abstract
Environmental problems and the related adaptation strategies have grown in \nimportance and complexity during the last years. The large amount of data and information \nthat needs to be handled and integrated requires specific methodologies and tools. Several \nresearch and application activities are undergoing worldwide for the development of \nDecision Support Systems (DSSs) that allow management of multiple and different data in \norder to aid decision-making. In this paper the following DSSs using fuzzy models based \nArtificial Intelligence to address environmental problems will be presented. SYRIADE is a \nSpatial DSS for Regional Risk Assessment of degraded land supporting the inventory and \nassessment of contaminated sites and mining waste sites at regional scale. DESYRE \naddresses the main phases of contaminated sites’ remediation process, e.g. analysis of \nsocial and economic benefits and constraints, site characterization, risk assessment, \nselection of best available technologies, analysis of residual risk and comparison of \ndifferent remediation scenarios. MODELKEY is a GIS-based DSS that supports the EU \nWater Framework Directive (WFD) implementation by allowing the environmental quality \nevaluation of fluvial ecosystems and the prioritization of hot spots along the river basin. \nFinally, the CMCC DSS supports the identification and prioritization of climate change \nimpacts and risks on coastal zones, in order to guide the definition of appropriate adaptations strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".