Bibliographic record
Abstract
We thank Dr Grant for his thoughtful comments on the CPS Practice Point ‘Nutrition for healthy term infants, birth to six months: An overview’. As stated, this Practice Point is a summary for the Paediatrics & Child Health readership of the full ‘Nutrition for Healthy Term Infants: Recommendations from Birth to Six Months’ (NHTI:0–6) available at www.hc-sc.gc.ca/fn-an/nutrition/infant-nourisson/recom/index-eng.php. This is a joint statement of Health Canada, the Canadian Paediatric Society, Dietitians of Canada, and Breastfeeding Committee for Canada to provide health professionals with evidence-informed guidance on early infant feeding for healthy term infants. We agree with Dr Grant that achieving optimal infant nutrition can lead to short-term and potentially long-term health benefits. The NHTI:0–6 recommends exclusive breastfeeding for the first six months. At six months of age, nutritious complementary foods should be introduced along with continued breastfeeding to help meet the nutrient requirements of the rapidly growing infant. Meat, meat alternatives and iron-fortified cereal are recommended as an infant’s first complementary foods because by six months of age, breast milk alone can no longer meet all of an infant’s nutrient requirements. Iron-fortified infant cereal continues to be recommended as an iron-rich first food; however, the recommendations provide a variety of examples of iron-rich first foods including meat, legumes and tofu in addition to iron-fortified infant cereals. Meat is an important source of iron because the type of iron contained in meat is highly bioavailable compared with plant sources such as legumes and cereal. This is particularly important for young infants because the amount of complementary foods they consume at this age is very small (only a few tablespoons per feeding). The infant feeding recommendations advise that “iron-containing foods”, which include vegetarian options, be offered two or more times per day.
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.029 | 0.023 |
| Insufficient payload (model declined to judge) | 0.066 | 0.043 |
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".