Development of seaweed cultivation in Latin America: current trends and future prospects
Bibliographic record
Abstract
Seaweed farming has now expanded across several continents from South East Asia to South America, Northern Europe, Canada and East Africa, contributing to global food security, supporting rural livelihoods, alleviating poverty and improving the health of oceans. Latin America (LA) covers a vast geographical area, which includes four different oceanic domains (Temperate Northern Pacific, Tropical Eastern Pacific, Temperate South America and Tropical Atlantic) and encompasses many types of coastal ecosystems with a wide range of seaweed species. LA has major potential for the development of seaweed farming activities; however, almost all the production is based on the harvesting of natural beds. This review describes the development of and prospects for the aquaculture seaweed industry in LA. The status of the seaweed aquaculture sector for green, brown and red seaweed and the main industry challenges are addressed. Regulation in the primary countries is also discussed. The expansion of the aquaculture industry in this region can be improved with new strains and farming methodologies, diversification of species, market expansion and an increase in domestic demand.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".