Marine Macroalgae for Industrial Extraction of Valuable Biofunctional Compounds Using Biorefinery
Bibliographic record
Abstract
According to the latest data from Food and Agriculture Organization ( FAO, 2017 ), 730,575 tons of macroalgae were yearly harvested worldwide. Chile alone was responsible for 36% of the worldwide macroalgae harvesting. Norway followed with 20% of the macroalgae world harvest, followed by Japan, Indonesia, Peru, and Canada. In 2015, macroalgae production reached 4,356,863.47 tons. Major worldwide production was achieved by China, producing almost 50%, mainly constituted by Wakame, Gracilaria , and indistinct macroalgae. Indonesia, the second world producer of aquaculture macroalgae reaching 19% of total, which mainly corresponds to the production of Eucheuma macroalgae for the extraction of carrageenan. Korea, Chile, and the Philippines are the following countries after China in macroalgae aquaculture production, respectively. The biorefinery concept is intrinsically connected with high-efficiency fractionation of biomass and the production of valuable biofunctional compounds. It represents a sustainable multi-process, transforming biomass into various marketable products and energy. Several strategies were developed for numerous industrial crops or biomass applications. Nowadays, chemical production or extraction using macroalgae as feedstock is mainly focused on single products, such as the extraction and purification of hydrocolloids, polysaccharides, pigments, proteins, and biofuels production, discarding the remaining biomass. Integrating sustainable strategies for cascade processing with efficient disintegration of biomass to obtain valuable biocompounds could be the key to a profitable industry. Several research works have been published using Gracilaria and Gelidiella genus for primary extraction of agar, bioethanol, and phycobiliproteins (PBP), with secondary extraction of fertilizers, lipids, bio-oil, biochar, and biogas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".