Farmers' perspectives for the development of a bioenergy industry in <scp>I</scp>reland
Bibliographic record
Abstract
Abstract A survey of Irish farmers was conducted to identify farmers’ opinions on energy crop production and to characterize potential adopters of energy crop cultivation in Ireland. One hundred and seventy‐two surveys were completed from 25 counties in Ireland. Miscanthus (48%) and grass (30%) were the preferred crops for adoption of energy crop production. Potential adopters described themselves as having a significantly greater level of knowledge of energy crop production compared with other respondents. The results indicate that lack of interest in adopting energy crop production may be due to lack of knowledge regarding the economic benefits of adoption and the variety of energy crops available for cultivation in Ireland. The establishment of long‐term contracts and government schemes were identified as important requirements for the development of bioenergy crop production in Ireland. Energy crop adoption was not limited to farmers undertaking specific farm enterprises. Farmers were motivated to adopt energy crop production for both economic and environmental benefits. These results are the first to provide valuable information on the perspectives of potential adopters of bioenergy crop production in Ireland for the promotion and implementation of a national bioenergy industry. Policy requirements and outreach strategies to encourage adoption of energy crops by agricultural producers are suggested.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".