The evolution and application of carrying capacity in aquaculture: towards a research agenda
Bibliographic record
Abstract
Abstract Carrying capacity has become a fundamental concept within the context of environmental management. Carrying capacity for aquaculture has been studied since the 1960s and has attracted a dedicated literature focused on measuring the environmental and production limits of aquaculture developments. Nevertheless, management and policy face emerging challenges across environmental and social aspects and the growing need to manage multiple objectives in increasingly crowded aquatic ecosystems. Therefore, promoting more sustainable aquaculture development should consider how the tools, methods and research used to support management and decision‐making should advance to meet such challenges. Here, the conceptual and practical applications of carrying capacity are reviewed and future prospects discussed. Carrying capacity for aquaculture has developed a range of models, indicators and approaches to study the relationships between aquaculture and ecosystem components. Carrying capacity supports diverse management objectives to support physical, production, ecological and social goals, although greater emphasis has focused on ecological and production capacities. This review introduces research needs and strategies to advance methods and tools and improve carrying capacity utilization for more holistic, ecosystem‐based aquaculture decision‐making. This paper presents a five‐pillar research agenda for carrying capacity that (i) recognizes system complexity and is (ii) policy‐relevant, (iii) adaptive, (iv) interdisciplinary and (v) meaningful. By promoting knowledge uptake and addressing literature gaps, the proposed agenda could help operationalize a holistic approach to managing for aquaculture sustainability.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".