Does green mean good? Evaluating the safety of microalgae dietary and protein supplements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent natural health food trends have seen a wide range of products promoted with a myriad of health claims. This has led to the rise of the green smoothie and dietary supplements, many of which contain microalgae – a broad category of photosynthetic microorganisms including spirulina (common name for Arthrospira platensis and A. maxima). Microalgae have been used as food items since the 1960 s in Japan and are currently marketed as a “Super Food”. Worldwide, the market for microalgae was valued at ≈ $608 million US dollars in 2015 with a 5%-7% increase in production to an estimated 27,552 tons with about $1.1 billion US dollars by 2024. This huge market is driven by the potential of the crop in biofuels, omega-3 fatty acids, metal chelators and other products. Microalgae contain a high percentage of protein and bioactive peptides as well as phycobiliproteins and carotenoids. The objective of this study was to determine whether microalgae supplements contain the neurotoxin N-β-methylamino-L-alanine (BMAA) or its isomers N-(2-aminoethyl)glycine (AEG) and 2,4-diaminobutyric acid (DAB). BMAA was detected in 4 out of 5 supplements containing spirulina at a maximum concentration of 0.74 µg/g using the AOAC validated method. AEG and DAB were detected in all 5 samples at a maximum concentration of 6.48 µg/g and 107 µg/g respectively. Subsequent studies including spirulina products from Hawaii, China and the United States demonstrated that BMAA, AEG and DAB were present in all samples tested at a maximum concentration of 0.32 µg/g, 2.81 µg/g and 43.5 respectively. These studies demonstrate the need for strict quality control of microalgae food supplements as well as more clinical trials to evaluate their health effects before they can safely be brought to the market.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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 it