Maladaptation to Climate Change Poses a Threat to Future Aquaculture Production
Bibliographic record
Abstract
ABSTRACT As the effects of climate change become more prominent and impacts intensify, the aquaculture sector must make decisions on adaptation strategies and implement actions that will reduce risks and increase resilience. However adaptation is complex, and there can be many consequences of action or inaction. One of the major risks is maladaptation, when an action that is introduced to minimize a negative effect makes the situation worse, increases vulnerability, or has other undesirable impacts. This study considers how climate change maladaptation can occur across six defined Aquaculture Maladaptation Outcomes: (1) Increased emissions of greenhouse gases, (2) Negative impact on farmed species, (3) Negative ecological or environmental impact at local, regional or international scale, (4) Negative social impact on individuals, communities, or the global population, (5) Negative economic impact on individuals, companies, or the global food market and (6) Reduced adaptive capacity of aquaculture systems. The study further explains that maladaptation could arise through different routes, often unintentionally and could occur at all stages in the production line and associated supply chain (e.g., feed production), such as the farm‐level or industry‐wide scale, threatening future food production and sustainability of the sector. The distinction between adaptation and maladaptation is not always clear, changing over time and influenced by different factors, so the adaptation‐maladaptation continuum is also discussed, as is the need for trade‐offs. Finally, seven recommendations are outlined to help advance adaptation to climate change in aquaculture.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 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".