Design and optimisation of a soybean supply chain network under uncertainty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Demands of foods have been increased in recent years for human and animal nutrition. Food supply chain management has been required to administer series of products and services in efficient ways for agriculture and food production to achieve customer satisfaction at the lowest cost. Agricultural systems have been changed during recent years, and have caused improvements in consumption and production patterns. However, there is not much research on supply chains of seeds (e.g., soybean) which have been produced in Canada. In this research, we propose a new mixed-integer linear optimisation formulation for a soybean supply chain network including multiple growers, farm facilities, distributors, and customers. The profit is maximised in the objective function. The application of the proposed formulation is discussed in Ontario in Canada using Google Maps. The mathematical model is developed by a unique possibilistic approach to include uncertain parameters. It is noticeable that uncertainty has been ignored in several papers in the food supply chain literature. Then, the proposed model is extended to a bi-objective model for the purpose of considering the organic practices (e.g., organic farming). The results of this research are discussed and analysed for the soybean supply chain network.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it