Strategic resilience: Integrating scheduling, supply chain management, and advanced operations techniques in production risk analysis and technical efficiency of rice farming in flood-prone areas
Bibliographic record
Abstract
Farmers face various risks such as production risks in the use of technology, pests, climate change and natural disasters. Farmers in disaster-prone areas have different responses depending on their behavior towards the risks posed. The main problem in this research is how farmers behave towards production risks due to flooding and the technical efficiency of rice farming in flood-prone areas. The aim of this research is to analyze farmers' behavior towards production risks due to flooding and the technical efficiency of rice farmers in flood-prone areas. The results of this research will provide important information for policy simulations that the government can implement towards farmers affected by natural disasters and for sustainable disaster mitigation strategies. The novelty of this research is that it combines two theories, namely risk behavior and agricultural technical efficiency in producing disaster mitigation strategies. The research location was determined purposefully in Pasuruan and Bojonegoro Regencies. The data in this research are primary and secondary data with the sample in this research being farmers. The sampling technique in this research is a multi-stage cluster sampling technique. The analysis method in this research uses Just Pope. and the Cobb-Douglas production function model with the Stochastic Production Frontier approach. The target of these research findings is a model of the types of behavior regarding the risks of farmers who are flood victims, as well as the level of technical efficiency of rice farming and the factors that influence it. The expected findings are policy recommendations regarding disaster mitigation from economic and agricultural risk aspects to create sustainable agriculture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".