The Feasibility of Controlled Environment in Horticulturally Poor Region: The Case of New Brunswick in Canada
Bibliographic record
Abstract
More than 90% of the money spent on food in the Canadian province of New Brunswick was spent on food that was imported to the province from either other provinces or out of the country. The feasibility of controlled environment agriculture in the Canadian province of New Brunswick depends on a large variety of factors, some of which have no available data. Few studies have looked at this issue, including consumers’ willingness to pay for locally grown produce in that region. The study aims at understanding how agriculture can serve the region differently to increase its food autonomy and how consumers would be receptive to more locally grown produce. From the information in the survey conducted, unless CEA (Controlled Environment Agriculture) crops can compete with conventionally grown and imported alternatives pricewise, it could face many issues in New Brunswick and Canada considering the economic uncertainties surrounding COVID-19. Canadians were also surveyed specifically about paying a premium for food that they considered local, not necessarily Canada as a whole, and many of the larger regions in Canada, such as Ontario and Quebec, consider food grown within their region as local – a definition which would not include New Brunswick.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".