The Consumption of Frozen Fruit and Vegetables in the Context of Malnutrition and Obesity; New Brunswick, Canada
Bibliographic record
Abstract
Malnutrition, reflected in the prevalence of obesity, is increasingly affecting the developed countries. For the first time in human history the number of people in the world who are overweight approaches that of the underweight. Faced with the economic and personal cost of chronic diseases caused by obesity, public health organizations are promoting increased consumption of produce (fruit and vegetables): in most of Europe and North America (as well as many developing countries where obesity has become a challenge), people are not eating the minimum recommended by the World Health Organization. The reason for low consumption of fruit and vegetables may be affordability, but low consumption may also be due to other factors. Among these could be availability, convenience or a perception that alternatives to fresh produce (such as frozen produce) are less nutritious. This paper focuses on frozen produce by asking consumers to compare it with fresh produce. Highlighting concerns that inhibit consumption of frozen fruit and vegetables could benefit public health. A random survey is undertaken to determine preferences between fresh and frozen produce, with their attributes ranked according to Analytic Hierarchy Process. The context is a province in Canada, New Brunswick, but it is hoped lessons can be transferred to other jurisdictions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".