Disentangling the Links between Energy and Agricultural Markets: The Shale Gas Phenomenon
Bibliographic record
Abstract
Technological developments in recent years, especially the 'fracking' technique, have allowed for a economically profitable extraction of shale gas, evolving into an increasingly important source of energy in the United States. Agriculture is increasingly more linked energy markets, traditionally through the input side (i.e. energy and fertilizer costs), but since the 2000s also through the production of biofuels. To analyse the potential effects on agricultural markets of the 'shale gas boom', a scenario analysis is carried out with the Aglink-Cosimo model. This scenario depicts a situation where the North America (US and Canada) benefits from certain energy price advantage versus the rest of the world. Our analysis shows a sizeable gain in competitiveness for US crop producers, with average production costs in the US decreasing considerably over the baseline period. These lower costs of production are expected to trigger lower producer prices and higher production, especially for energy intensive crops such as maize, sorghum and sugar beet. However, the presence of uncertainty regarding the future development of crude oil prices can considerably affect these margins.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".