Public preferences for planting genetically improved poplars on public land for biofuel production in western Canada
Bibliographic record
Abstract
We examine public opinion of planting genetically improved poplars on public lands in western Canada. Policy scenarios consider the use of three different breeding methods (traditional selective breeding, genomics-assisted breeding, and genetic modification), each with and without poplars being used for biofuels. We employ a choice experiment to provide alternative outcomes to policy scenarios and to investigate differences among characteristics of respondents. Overall, a majority of respondents voted in favour of policies that allowed improved poplars on public land if the fibre is used to generate biofuels. Adding biofuel production to a policy scenario increases the probability of acceptance by 17%–32%. In contrast, the various types of breeding technology do not matter as much regarding public acceptance. Responses differ among segments of the population, but these differences do not greatly influence choices. Attributes that increase the probability of acceptance are being a male, being from Alberta, and being from a population centre of 10 000–100 000 people (relative to centers that are >100 000 people). Attributes that decrease the probability of acceptance are age, being from British Columbia, and being from a population centre of <10 000 people (relative to centers that are >100 000 people). Despite these significant patterns of preferences, there is substantial uncertainty underlying the responses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".