Global estimates of suitable areas for marine algae farming
Bibliographic record
Abstract
Abstract Marine algae, both macro and micro, have gained increasing attention for their numerous ecosystem service functions, such as food and raw materials provision and climate change mitigation. Currently, the practice of large-scale algae farming is limited to Asian waters, but significant interest has arisen from other continents. However, there is a lack of knowledge about the areas with suitable environmental conditions for expanding algae farming on a global scale. Previous studies have primarily focused on nutrient availability and thermal constraints when assessing the potential for algae culture. This study uses species distribution models based on an ensemble consensus approach to determine the extent of suitable areas and takes into account multiple environmental factors that may affect the feasibility of algae culture. Our results show that approximately 20.8 million km 2 of the ocean (∼13.8% of the economic exclusive zones) is suitable for farming marine algae species, with most potential areas located near the coastline. Surprisingly, four out of the top five countries with the largest area suitable for seaweed farming, including Australia, Russia, Canada, and the US, account for 30% of the total suitable areas, yet they currently produce less than 1% of the global seaweed. Several species show promising characteristics for large-scale cultivation, but their viability for commercial production remains uncertain and subject to further assessment of economic feasibility and social acceptance. Further research on the ecological benefits of seaweed farming could also promote the development of an ecologically friendly and financially viable algae mariculture industry. This study provides a scientific basis for decision-makers to understand potential expansion areas and feasible pathways for seaweed farming, with the ultimate goal of ensuring the sustainable utilization of marine resources.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".