Environmental risk management with the aid of city emergency response system in Nanning City
Bibliographic record
Abstract
Environmental risk management (ERM) is updated on the city emergency response system (CERC) in Nanning City, China. The technical routine developed better support effective urban ERM in the Nanning CERC. Started from identification of risk sources, programs of sources monitoring, risk prediction and early warning, treatment and disposal, and management with update were discussed. Furthermore, environmental risks posed by the China-ASEAN (Association of Southeast Asian Nations) Expo, an international trade fair were evaluated. The inverse searching technique was used to identify the hazardous sources that can cause risks at the Expo. The paradigm of ERA facilitates investigating the connections between hazard sources and adverse effects for people involved in the Expo. Sensitivity amongst people involved during the Expo was determined according to human oriented characteristics. Temporal and spatial sensitivities of the Expo related to the environmental risks were defined. The developed methodology has successfully safeguarded the China-ASEAN Expo from 2004 to 2008. This work highlights major steps in the procedure for update on the CERC with ERM, which provides a demonstration case for integrating urban emergency response and environmental management with functional enhancement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".