Exploring the Adoption of Decision-Support Tools in Ontario Rainbow Trout Farming Using SWOT and AHP Analysis
Bibliographic record
Abstract
Decision-support tools (DSTs) are gaining traction in various industries, including agriculture, to enhance productivity, optimize resource utilization, and improve overall farm management. In the context of rainbow trout farming, two DSTs, AquaManager, and AquaOp Farm Management System, have emerged as potential tools to address the challenges faced by Ontario-based producers. This study aims to investigate the potential adoption of these two DSTs by employing the Strengths, Weaknesses, Opportunities, and Threats (SWOT) and Analytic Hierarchy Process (AHP) methods. The SWOT analysis will provide a comprehensive overview of the internal and external factors influencing the adoption of AquaManager and AquaOp. This includes strengths such as cost-effectiveness, data integration capabilities, and user-friendly interfaces, as well as weaknesses related to technical complexity, initial investment costs, and reliance on internet connectivity. Opportunities include the increasing demand for sustainable and efficient aquaculture practices, government support for DST adoption, and the potential to improve farm profitability and environmental sustainability. Threats include privacy concerns, compatibility issues with existing farm systems, and the potential for cybersecurity risks. The AHP will be employed to systematically assess the relative importance of the various SWOT factors and evaluate the overall suitability of AquaManager and AquaOp for Ontario rainbow trout farmers. This involves pairwise comparisons of factors based on their impact on the decision-making process, allowing for a clear prioritization of factors and identifying critical success factors for successful DST adoption. The findings of this study will provide valuable insights into the factors influencing the adoption of AquaManager and AquaOp in Ontario rainbow trout farming. This information can be utilized to develop targeted strategies to promote DST adoption, enhance farm performance, and contribute to the sustainability of the Ontario aquaculture industry.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".