Macroeconomic Impact of Establishing a Large‐scale Fuel Ethanol Plant on the Canadian Economy
Bibliographic record
Abstract
This paper analyzes the macroeconomic impact of establishing a large‐scale fuel ethanol plant that uses com as its feedstock. An input‐output model of the Canadian economy is used to estimate the macroeconomic impact. The agricultural output data required for the input‐output analysis are generated using an econometric model. The macroeconomic impact of operating this plant is increases in industrial output of $328.6 million, in GDP at factor cost of $84.2 million and in employment of 1,390 jobs. Ce document a pour but d'analyser l'impact macro‐économique de l'installation d'une grande usine productrice d'éthanol, utilisant le maï's comme matière première. Un modèle entrées‐sorties de l'économie canadienne est employé afin d'estimer l'impact macro‐économique. Les données de rendements agricole requises pour cette analyse sont générées à l'aide d'un modèle économétrique. Les impacts macro‐économique qui résultèrent suite à l'opération de cette usine sont: un augmentation de $328.6 millions du rendement industriel, une augmentation de $84.2 millions du PIB au coût des facteurs, ainsi qu'un gain de 1,390 emplois.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".