Measurement of haem and total iron in fish, shrimp and prawn using ICP-MS: Implications for dietary iron intake calculations
Bibliographic record
Abstract
Twenty-five species of fish, shrimp and prawn from local markets in Bangladesh were analysed for concentrations of total Fe, haem Fe and non-haem Fe by ICP-MS. Total Fe and non-haem Fe concentrations were measured in nitric acid-digested samples and haem Fe was extracted using acidified 80% acetone for 60 min. Total Fe concentrations ranged from 0.55-14.43 mg/100 g FW, and haem Fe% ranged from 18%-93% of total Fe. Repeat extractions with 80% acetone recovered additional haem Fe, suggesting that previous measurement by this technique may have underestimated haem Fe content. Calculation of Fe balance (summing Fe in acetone extracts and Fe in the residue after haem Fe extraction) was not significantly different from total Fe, indicating the two processes recovered the different forms of Fe with similar effectiveness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".