Bibliographic record
Abstract
Low-trophic aquaculture (LTA), including seaweed and bivalve farming, is often promoted as an environmentally sustainable food production system due to its low input requirements and potential ecological benefits. However, this sustainability narrative is increasingly undermined by the pervasive use of plastic-based gear. This systematic review of 1,768 peer-reviewed publications (2003–2024) reveals that synthetic polymers remain the dominant material in LTA infrastructure, contributing to marine litter, microplastic pollution, and long-term ecological degradation. Although one-third of studies mentioned farming gear, only 6 % (n = 108) disclosed the material composition, indicating a critical gap in sustainability reporting. Among these, over 70 % referenced conventional plastics such as polyethylene, polypropylene, and nylon. Regional and species-level differences emerged: natural materials were more frequently reported in Asian seaweed farming, whereas non-Asian studies were more likely to explore biodegradable or bio-based alternatives. Nevertheless, plastics remained prevalent across all contexts, particularly in bivalve systems (up to 88.2 % in Asia). Sentiment analysis of abstracts revealed regional differences in research framing, with Asian studies adopting a more pragmatic tone and non-Asian studies engaging more critically with environmental issues. These findings underscore the urgent need to address plastic dependence in LTA and promote scalable, regionally tailored alternatives through improved policy, reporting, and innovation pathways. • Most low-trophic aquaculture (LTA) systems still rely heavily on plastic gear. • Only 6 % of sustainability and farming studies mention gear material in abstracts. • Asia leads in production but lags in sustainable gear research. • Abstract tone differs by region, with more critical reflection outside Asia. • Sustainable alternatives exist but need policy and industry support to scale.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".