CRFM: An application of the fuzzy logic in apell programme
Bibliographic record
Abstract
The Awareness and Preparedness for Emergencies at Local Level (APELL) Programme was initiated in the late1980s in response to a number of chemical accidents that resulted in deaths and injuries, environmental damage, and extensive economic consequences in the surrounding communities.Initially, the APELL Programme focused on assisting decision-makers and technical personnel in improving community awareness of industrial hazards and in preparing response plans for chemical accidents.Nowadays, it is also applicable to natural hazards.This paper aims to provide to community, locallevel institutions, industry, experts and other stakeholders a tool, using Fuzzy Relation, the Community Risk Fuzzy Model (CRFM) to estimate the various types of risk they are exposed to, supporting the decision-making process, especially as to whether or not further assessments are needed.This tool will be developed based on an existing one, named Community Risk Profile (CRP), of the United Nations Environment Programme (UNEP).An application of CRFM will be presented using three communities at risk of flooding located in the metropolitan area of the City of Rio de Janeiro, Brazil.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".