Bibliographic record
Abstract
Global demand for fuel ethanol has increased significantly over the last decade. The U.S. was a net importer of ethanol until 2009. However, the U.S. has emerged as a net exporter since then. Accordingly, it is important to analyze the bilateral ethanol trade between U.S. and its major export destinations (Canada and Brazil). Main objective of this study is to identify factors influencing the bilateral U.S. ethanol trade with Canada, and Brazil as well as analyzing the impacts of these factors on U.S. ethanol trade with Canada, and Brazil. A system of simultaneous equations consisting of U.S. net export of ethanol to Canada, and Brazil are estimated. Using quarterly time series data for 2001- 2012 time periods, U.S. net ethanol export model was estimated with three-stage least squares (3SLS). The results suggest that U.S. net exports of ethanol to Canada, and Brazil are mainly driven by ethanol mandates rather than world crude price. U.S. net export of ethanol to Canada was impacted significantly by Canadian GDP and insignificantly by U.S. ethanol price. In contrast, the impact of Brazilian GDP on U.S. net ethanol export to Brazil was insignificant and that of relative price of ethanol was significant. Ethanol related policy variables including Canadian participation in Kyoto protocol, Canadian federal ethanol producer and consumer incentive, and world sugar prices were found not to have any statistically impact on U.S. net ethanol exports to Canada, and Brazil.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".