Sustainable Agriculture and Climate Change: Producing Potatoes (Solanum tuberosum L.) and Bush Beans (Phaseolus vulgaris L.) for Improved Food Security and Resilience in a Canadian Subarctic First Nations Community
Bibliographic record
Abstract
Aboriginal people in Canada experience disproportionately high rates of diet-related illnesses, such as obesity and diabetes. Food insecurity has been identified as a contributing factor to these illnesses along with a loss of traditional lifestyle. Current food systems within northern subarctic and arctic regions of Canada rely heavily on imported foods that are expensive (when available), and are environmentally unsustainable. A warming subarctic and arctic climate present challenges, but also offers the opportunity for local agricultural production that can increase food security and promote a more sustainable food system. In this study the feasibility of sustainably growing potatoes (Solanum tuberosum L.) utilizing agroforestry practices to enhance food security in remote subarctic communities is explored through a case study in Fort Albany First Nation in northern Ontario, Canada. Potato crops were grown over a two-year period and rotated into plots that had been planted with green bush beans (Phaseolus vulgaris L.). Results showed that potatoes and bush beans could be grown successfully in the subarctic without the use of greenhouses with yields comparable to more conventional high-input agricultural methods. In subarctic Canada, sustainable local food production can help to promote social capital, healthier lifestyles, and food security.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".