Climate change adaptation in aquaculture
Bibliographic record
Abstract
Abstract This study conducts the first systematic literature review of climate change adaptation in aquaculture. We address three specific questions: (i) What is aquaculture adapting to? (ii) How is aquaculture adapting? and (iii) What research gaps need to be addressed? We identify, characterise and examine case studies published between 1990 and 2018 that lie at the intersection of the domains of climate change, adaptation and aquaculture. The main areas of documented climate change impacts relate to extreme events and the general impacts of climate change on the aquaculture sector. Three categories of adaptation to climate change are identified: coping mechanisms at the local level (e.g. water quality management techniques), multilevel adaptive strategies (e.g. changing culture practices) and management approaches (e.g. adaptation planning, community‐based adaptation). We identify four potential areas for future research: research on inland aquaculture adaptation; studies at the household level; whether different groups of aquaculture farmers (e.g. indigenous people) face and adapt differently to climate change; and the use of GIS and remote sensing as cost‐effective tools for developing adaptation strategies and responses. The study brings essential practical and theoretical insights to the aquaculture industry as well as to climate change adaptation research across the globe.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".