Mapping the global mass flow of seaweed: Cultivation to industry application
Bibliographic record
Abstract
Abstract The global flows of cultivated seaweed were estimated for the year 2019 using a combination of literature review, assumptions, and simple conservation of mass calculations. Red seaweeds were found to be the largest contributors to the hydrocolloids industry, for both food and non‐food applications. Carrageenan‐containing species were found to be the largest contributors to both food (62%) and non‐food (55%) hydrocolloids and are the primary source for water gels, which make up 27% of non‐food hydrocolloids, followed by pet food (16%), toothpaste (6%), and others (6%). Carrageenan also accounts for almost all meat products, which make up 35% of the food hydrocolloid industry, and dairy products, which make up 26%. Agar‐containing seaweeds are used in confections (10% of food hydrocolloids), baking (9%), and other (2%) and make up 15% of non‐food hydrocolloids. Porphyra (nori) is cultivated for direct consumption and makes up 23% of direct food consumption. Cultivated brown seaweeds were found to comprise Laminaria/Saccharina for alginate production (30%), Laminaria/Saccharina for direct consumption (44%), and Undaria for direct consumption (16%). About half of the alginates produced make up 18% of food hydrocolloids, and the other half is used in non‐food hydrocolloids comprising technical grades (28% of non‐food) and animal feed (3%). The results are discussed in the context of emerging markets for seaweed and the potential for seaweeds as a substitute for staple foods, and the environmental impact of seaweed farming is explored through a review of life cycle assessment studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".