COOKNOOK: Intelligent Meal Planning Application
Bibliographic record
Abstract
In Canada, nearly 40% of university students experience food insecurity [1, 2]. However, most young adults and students are not aware or even underestimate the extent of the insecurity they face. This phenomenon can increase the cognitive burden associated with food management, leading to poor resource allocation exacerbating levels of food insecurity. This negatively impacts Sustainable Development Goal 2, 'Zero Hunger.' The Canadian government and university institutions have often relied on food banks or charitable organizations. However, these interventions have mainly served as emergency measures rather than long-term solutions [3]. Through research based on a design-adapted ethnographic approach, our team has gained a deep understanding of the situation of university students and the challenges related to food. Consequently, we propose CookNook: an application featuring an intelligent personal assistant aimed at helping users prioritize cooking time by reducing the cognitive load associated with food. This is accomplished by providing simple and accessible recipes, accompanied by shopping lists tailored to their resources, needs, and constraints. CookNook also facilitates culinary gatherings with friends to share meals while motivating them to cook more. In this article, we summarize our design process and how our solution represents a step towards ensuring sufficiently quality nutrition among university students.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".