Improving nutritional intakes and reducing metal(loid) exposures from wild fish broth among Inuit pregnant women
Bibliographic record
Abstract
Recipes made with local country foods are central to Inuit culture and nutrition. Recipes are recommended for their different health benefits, such as a wild fish broth recipe that is said to help the baby's growth during pregnancy and with lactation. However, some country foods can have high concentrations of potentially toxic metal(loid)s such as mercury (Hg), arsenic (As) or cadmium (Cd), and it is unknown to what extent these are transferred to the broth. During pregnancy, there are higher risks of developing iron (Fe) and calcium (Ca) deficiencies. A simple way to optimize the nutrient content of recipes is by adding other ingredients like seaweed, bivalves (mussels and clams) or a Lucky Iron Fish® known to be rich in these nutrients. Using an experimental approach, nutrient (essential elements and fatty acids) and metal(loid) transfer to broth were studied by measuring their concentrations in ingredients and broth. Most fish, seaweeds and bivalves were important sources (>20 % of the daily intakes) of nutrients required for healthy pregnancies. Several nutrients were transferred to the broth by these ingredients, but only fish broth was an important source of nutrients. The Lucky Iron Fish was a potential source of iron when preconditioned. Total Hg concentrations were elevated in lake trout muscles and cheeks (up to 4.5 μg/g ww; >90 % in methylated form) but were not a concern in other fish species. Few metal(loid)s were transferred to the broth, except arsenic (As). Total As concentrations were high in some raw seaweeds and most broths, but the less toxic organic forms were mainly found. Overall, wild fish broths were relatively low in nutrients and toxic forms of metal(loid)s. Adding local ingredients such as seaweed and bivalves could increase nutrient intake from fish broth if consumed as a whole.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".