The Economics of Genomic Information Sharing in the Alberta Beef Sector
Bibliographic record
Abstract
Abstract In this thesis, I estimate the willingness-to-accept (WTA) for genomic information sharing in Alberta beef cattle production. In addition, I examine the factors that influence the WTA by commercial cow-calf producers in genomic information sharing with Breeding Associations (BAs). As part of this thesis, I conducted a survey among the commercial cow-calf producers in Alberta. In total, 52 respondents completed the survey. Through the survey, I find that educational background, farm size, farm operation and the type of information all affect the willingness to share information. Larger farms and those that already have systems in place to collect genomic data easily are more willing to share their information at a lower price. Furthermore, respondents with genomic-related majors are more willing to share their information. Lastly, the perception about the benefits of genomic information sharing also influences respondent behavior. My results show that paying producers can be an effective tool to encourage cow-calf producers to share genomic information with BAs. However, the type of information and the degree of difficulty associated with collecting the information matter a great deal.
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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 | 0.002 | 0.006 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".