Chemical Composition of Common Seaweeds from the Kenya Coast
Bibliographic record
Abstract
The gross nutritional profile of 34 seaweed species from three sites (Mkomani, Kibuyuni and Mtwapa) in coastal Kenya were studied. The crude fat, crude protein, crude fibre and crude ash were determined by the standard AOAC methods while the nitrogen-free extract (NFE) was calculated by weight difference of the chemical components. The chemical constituents of the seaweeds varied significantly among the algal divisions, species, months and sites (p<0.05). The major chemical components was the NFE with a mean value of 42.09 ± 0.83% dry weight (DW) followed by crude ash (31.94 ± 0.78% DW), crude fibre (14.08 ± 0.26% DW), crude protein (10.09 ± 0.26% DW), whereas the least component was crude fat with a mean value of 1.81 ± 0.04% DW (p<0.05). The crude protein levels were positively correlated with nitrogen content and in crude fibre and negatively with NFE, crude fat and crude ash (p<0.05). The findings on the gross nutritional profile of the seaweeds in this study could be used as a basis for more advanced research on nutritional information guideline and as potential resources for seaweed-based products for improved human and animal nutrition.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".