<i>Estimated Absorbable Iron Intakes</i>In Quebec Adults
Bibliographic record
Abstract
In the last 30 years, the prevalence of low dietary iron intake has increased, partly because North Americans have been encouraged to choose lower fat foods, including leaner meats. As a result, there has been a decrease in red meat consumption. The objective of this study was to estimate absorbable iron intakes of a representative sample of the Quebec adult population and to compare these results with the new North American recommendations for iron. Dietary intakes were obtained by 24-hour recall and absorbable iron intakes were estimated using Monsen & coworker's model. While 18.9 % and 2.5% of women and men respectively had an iron intake below the estimated average requirement (EAR), 66.2 % and 25.6% had an estimated available iron intake below the recommended level for absorbed iron. Beef consumption had the strongest association with estimated absorbable iron, followed by vegetables. Subjects with a high education level consumed significantly less meat, beef and heme iron than subjects whose education level was low or moderate. These results indicate that there is a need for improving iron intake in the Quebec adult population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".