Fucoidan Content in Philippine Brown Seaweeds
Bibliographic record
Abstract
This study aims to determine which brown macroalgae in the Philippines has the highest content of partially purif ied fucoidan. Percent fucoidan content of brown seaweeds Sargassum spp. , Padina sp. , Hydroclathrus sp. , Turbinaria ornata J. Agardh, Hormophyza cuneiformis PC Silva, and Dictyota dichotoma Lamouroux were determined in f ifty sites across 14 provinces in Northern Luzon (Cagayan, Ilocos), West Luzon (Pangasinan), the eastern seaboard of Luzon (Quezon Province, Camarines, Sorsogon), Central and Eastern Visayas (Bohol, Cebu, Negros Oriental, Negros Occidental), and Northern Mindanao (Camiguin, Lanao del Norte, Misamis Oriental, Misamis Occidental). Crude and semi-pure fucoidan were extracted through acid hydrolysis and ethanol precipitation using 50 grams of dried and milled seaweed biomass. Extracts were verif ied using infrared spectroscopy with fucoidan from Fucus vesiculosus as standard. Sargassum spp. is the most widely distributed source of fucoidan found in all sites. T. ornata was found in only 11 sites. Both have significantly higher percent content (p ≥ 0.05) of fucoidan than other sampled seaweeds. Higher percent content of semi-purified fucoidan were observed in D. dichotoma from Bohol (1.53%), H. cuneiformis from Cebu (2.17%), Hydroclathrus sp. from Pangasinan (2.23%), Padina sp. from Quezon Province (3.69%), Sargassum spp. from Camiguin (4.30%), and T. ornata from Cagayan (7.03%). Keywords:
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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 | 0.000 | 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".