Navigating the Digital Frontier: An Exploration of Technology Adoption in Ontario's Rainbow Trout Farming Industry
Bibliographic record
Abstract
Rainbow trout farming in Ontario, Canada, has seen a relatively low adoption of digital technology, despite the potential benefits that technology can bring to the industry, such as improved efficiency, enhanced traceability, and better decision-making capabilities. Recognizing the significance of digital technology in management decisions, this study analyzed the viewpoints of farmers, researchers, technology providers, and other stakeholders to comprehend the hindrances preventing rainbow trout producers from adopting digital technology. Utilizing Q-methodology with various stakeholders, this study endeavors to uncover the critical factors obstructing technology integration in Ontario's rainbow trout farming operations. The study will focus on understanding the current state of technology adoption, the reasons for non-adoption, the potential benefits, and the support systems needed to encourage farmers to adopt the technology. The study will also investigate the challenges farmers face when embracing technology, such as a lack of understanding of the technology, high costs, and lack of technical support. Additionally, the study will explore the potential benefits farmers could gain by adopting digital technology and how it can improve their operations. The results of this study will be valuable to policymakers and industry leaders as it will provide a better understanding of the challenges and opportunities associated with adopting digital technology in the rainbow trout farming sector in Ontario, Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".