Effect of disaster events on regional food security spatially: A geographically weighted regression model
Bibliographic record
Abstract
Natural disasters are one of the events that threaten food security in every region in Indonesia. Ciamis District is one of Indonesia's areas prone to natural disasters. One of the impacts of a disaster is on the agricultural sector because it will determine food security in the area and can affect the socio-economic community. This study aims to analyze the Geographically Weighted Regression (GWR) Model of the Effect of Disaster Events on Food Security. This study used quantitative methods, and data sources were obtained through secondary data searches. Data were processed using a Geographically Weighted Regression (GWR) analysis tool through a quantitative descriptive analysis approach. The results showed that the natural disasters directly or indirectly impacted food availability and security in Ciamis District. Natural disasters still occur even though food availability, affordability, and utilization are quite good. There are other factors, namely disasters, that come suddenly and cannot be predicted, thereby affecting the food security of a region. Efforts are needed to create togetherness in society to anticipate and respond to disaster events. In addition, it is expected that each individual can take the necessary actions to solve problems independently.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".