Seafood Consumption and Its Contribution to Nutrients Intake among Canadians in 2004 and 2015
Bibliographic record
Abstract
Fish and seafood are excellent sources of nutrients such as omega-3 fatty acids, Vitamin D, and selenium. The aims of this study were to examine the pattern of seafood consumption among Canadians and determine their contribution to intakes of energy and nutrients. Day-1 24-h dietary recalls data collected from a national survey, the Canadian Community Health Survey—Nutrition in 2004 and 2015, were used to estimate food and nutrient intakes among Canadians. Seafood was classified according to the Bureau of Nutritional Sciences food list. Descriptive statistics were used to calculate the consumption rate and the average consumption amount of seafood by different age groups and sociodemographic characteristics. Population ratios were used to assess the contribution of seafood to the total intake of energy and nutrients. The overall consumption rate of seafood was around 17%, and the rate was similar between males and females, and slightly higher in 2015 (17.71%) compared to 2004 (16.38%). The average portion size is approximately 100 g, which translates into a ≈36 kg annual intake among the consumers and ≈6.2 kg per capita consumption. Adults (especially 30 years and above), Asians, individuals who were married, and with post-secondary education were more likely to consume seafood. Salmon, tuna, shrimp, cod, and crab were the most frequently consumed seafood in Canada, the consumption rate of which all increased from 2004 to 2015. Seafood provided up to 75% of n-3 PUFAs, 18% of Vitamin D, 19% Vitamin B12, 6% of niacin, and 4% of Vitamin B6 from all food sources. Seafood consumers had a healthier diet, as seafood consumption was related to a higher intake of key nutrients and a lower intake of total sugar and saturated fatty acids. Therefore, fish consumption should be promoted among Canadians.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".