Establishing heme iron values for the Canadian Nutrient File (CNF)
Bibliographic record
Abstract
Objectives Heme iron, primarily from the hemoglobin and myoglobin of meat, poultry and fish (MFP) is better absorbed than non‐heme iron. Monsen et al. (1978) assumed that the proportion of heme iron contained in various meats averages 40%, while the remaining 60% is found in the less bioavailable non‐heme form. More recent evidence suggests that this ratio may vary depending on the type and cut of MFP. Currently, the CNF lists only total iron values. The objective of our research was to match heme iron values from the literature to foods within the CNF. Methods An extensive literature search was conducted to identify the heme content of different souces of MFP. Values from the literature were then matched to CNF foods with consideration of cuts and similarities to North American cooking methods. In order to match heme values from the literature to MFP foods, average values were often generated across cuts and cooking methods eg. ground chicken was an average of white and dark meat. Results and Discussion A broad range of heme values were observed across MFP types. The following heme values were attributed: 33–70% for beef cuts, 21–40% for fish, 27–66% for pork cuts, 26–37% for poultry, and 40–63% for veal. When no literature value for heme iron was found eg., game meats, and many types of fish, a value of 40% was used as a default. The heme content of beef, veal and some pork cuts are higher than 40%, while poultry and fish might have lower heme iron contents. Conclusion Incorporating heme iron values into the CNF that are reflective of various MFP provides useful data for assessing iron bioavailability from foods/diets and for developing future nutritional policies targeted at reducing iron deficiency.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".