A study on the location of emergency supplies reserve center in Guangxi for tropical cyclone disasters
Bibliographic record
Abstract
On average, Guangxi experiences 2 severe tropical cyclone disasters yearly. A large number of disaster victims need governments at all levels to raise enough emergency supplies to carry out effective relief. However, there is no proper reserve center for tropical cyclone disaster storage and collection of emergency supplies in Guangxi, so it is urgent to choose a suitable location to build the Guangxi tropical cyclone disaster emergency supplies reserve center. The data of 16 severe tropical cyclone disasters and rescues in Guangxi from 2014 to 2021 were collected. An optimal location model of the emergency supplies reserve center for tropical cyclone disasters in Guangxi was established using the improved center of gravity method based on GIS. After four iterations of calculation, the longitude and latitude coordinate S5(108.64, 21.98) were used as a suitable site to construct the Guangxi Tropical Cyclone Disaster Emergency Supplies Reserve Center to ensure the fastest and lowest cost delivery of emergency supplies to the disaster areas in Guangxi. According to the map of Tiandi, the actual place corresponding to this coordinate is near Liuwu Village next to Qinzhou East Railway Station, Qinnan District, Qinzhou, Guangxi. This location coordinate S5 can provide a reference for the Guangxi Zhuang Autonomous Region government to select a suitable address to construct the Guangxi Tropical Cyclone Disaster Emergency Supplies Reserve Center. The improved center of gravity location model based on GIS can also provide a reference for other provinces or cities to carry out emergency supplies reserve center locations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".