Integrated bioethanol-gasoline supply chain evolved by changing US Government policies - model and algorithm
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
COVID-19 travel restrictions caused gasoline consumption reduction. Global warming and crude oil dependency had already driven policymakers to make policies to reduce consumption of gasoline. The US had created policies to regulate bioethanol production and blending with gasoline. Although these regulations created opportunities, they also placed new burdens on the obligated parties. The effect of the policy change on the integrated bioethanol-gasoline supply chain (IBGSC) is therefore important for both government and business to study to reduce bankruptcies in current market refineries and bio-refineries. To that end, we extend the IBGSC studied by Ghahremanlou and Kubiak (2020a) to include both first and second generation bioethanol, import and export, and existing infrastructure. We develop a two-stage stochastic programming model. Solving this model leads toward solving NP-hard problems, therefore, we develop an algorithm and overcome the computational complexity. The ELM can be employed to evaluate sustainability of the IBGSC under different policies.
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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 | 0.002 | 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