Bibliographic record
Abstract
Worldwide, aquaculture is important for food security and nutrition (FAO, 2015). In Ontario, the aquaculture sub-sector is relatively small and dispersed (OMAFRA, 2015). Understanding the context, stakeholders and prospective actors are essential within this sub-sector. This requires 1) the identifying key stakeholders; 2) establishing the groundwork for the industry including capturing investment and development opportunities, and 3) establishing the foundation for a knowledge transfer network in relation to education and extension of potential aquaculture. Emphasis is placed on using an innovation brokerage model that involves diverse stakeholders (Klerkx et al., 2010). Ontario aquaculture is dominated by a small number of large farms. However, there are also organic aquaponics farms currently are a small sub-section of producers. Among this group of producers, there are op opportunities to grow businesses with capacity building and knowledge sharing. Therefore, my research goal is to use an innovative systems approach to identify, map and analyze the actor within Ontario’s aquaponic production and value chains in order to determine the needs of small and medium producers, including attention to the knowledge of science, capacity building, and networking potential. I intend to develop a methodology for collaboration and capacity building in order to support the achievement of socio-cultural and economic benefits of aquaponics in rural Ontario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".