Exploring Factors That Influence Perceptions of Using Genomics for Emission Reductions in Beef Cattle
Bibliographic record
Abstract
Given the quantity of greenhouse gas emissions resulting from beef production and rising concerns with climate change, genomics have been introduced to facilitate selective breeding for increased feed efficiency in beef cattle as one area of emissions reductions. Public perception is an important consideration in this endeavour. In this study data collected from a survey of 1803 participants from across Canada is analysed and the influence of attitudes and knowledge pertaining to the environment and biotechnologies on the degree of acceptance and relative perceived benefit to human health of this use of genetic technology is examined. Upon grouping respondents into categories of those who oppose, doubt, and support this use of genomics, multinomial logistic regressions are used to determine the factors influencing an opposing or supporting position, relative to doubt, the relatively neutral position. Results suggest that distinct characteristics influence the likelihood of supporting or opposing this use of technology with respect to two different measures of acceptability of the technology - degree of acceptance and relative perceived benefits to human health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".