Birds and Bioenergy within the Americas: A Cross-National, Social–Ecological Study of Ecosystem Service Tradeoffs
Bibliographic record
Abstract
Although renewable energy holds great promise in mitigating climate change, there are socioeconomic and ecological tradeoffs related to each form of renewable energy. Forest-related bioenergy is especially controversial, because tree plantations often replace land that could be used to grow food crops and can have negative impacts on biodiversity. In this study, we examined public perceptions and ecosystem service tradeoffs between the provisioning services associated with cover types associated with bioenergy crop (feedstock) production and forest habitat-related supporting services for birds, which themselves provide cultural and regulating services. We combined a social survey-based assessment of local values and perceptions with measures of bioenergy feedstock production impacts on bird habitat in four countries: Argentina, Brazil, Mexico, and the USA. Respondents in all countries rated birds as important or very important (83–99% of respondents) and showed lower enthusiasm for, but still supported, the expansion of bioenergy feedstocks (48–60% of respondents). Bioenergy feedstock cover types in Brazil and Argentina had the greatest negative impact on birds but had a positive impact on birds in the USA. In Brazil and Mexico, public perceptions aligned fairly well with the realities of the impacts of potential bioenergy feedstocks on bird communities. However, in Argentina and the USA, perceptions of bioenergy impacts on birds did not match well with the data. Understanding people’s values and perceptions can help inform better policy and management decisions regarding land use changes.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".