Consumers' attention on identification, nutritional compounds, and safety in heavy metals of Canadian sea cucumber in Chinese food market
Bibliographic record
Abstract
Abstract Background Based on the consumers’ attention issues of sea cucumbers, we aimed to complete comprehensive information of commercial Canadian sea cucumbers (CCSC), which sprang up extensively in Chinese food market. Results CCSC were identified as Cucumaria frondosa and characterized based on the characteristics, nutritional compositions, and heavy metals. The abdomen and five internal tendons of Cucumaria frondosa were special orange. The average of soaking degree and water content, which consumers paid great attention to, was 2.8 ± 0.3 and 0.46 ± 0.09%, respectively. Proteins (56.4 ± 9.1%) and polysaccharides (12.2 ± 14.7%) were the principal nutrient component. In addition, there was a variety of free amino acids, in which arginine (70.1 ± 50.0 mg/100 g), glutamate (42.6 ± 23.9 mg/100 g), and alanine (32.2 ± 21.0 mg/100 g) were the main components. Phosphorus (P, 0.26 ± 0.05%), magnesium (Mg, 0.19 ± 0.07%), and kalium (K, 0.17 ± 0.08%) were the major mineral elements. Amount of heavy metal was within the safety limitation (5.5 ± 1.4 mg/kg). Furthermore, the active ingredients were positively correlated with size. Conclusion The overall findings enriched the information of Cucumaria frondosa for consumers and suggested that the quality of Cucumaria frondosa was varied following commercial classification and size.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".