Fishery Livelihood Adaptation on Climate Change: A Bibliometric Analysis and Review
Bibliographic record
Abstract
The primary objective of this research is to provide a high-level overview of potential impacts and details of ongoing and completed adaptation measures on aquatic and marine ecosystems and the livelihoods they support as a result of climate change.Changes in acidity, sea temperature, and circulation patterns, the frequency and intensity of extreme events, sea level rise, and associated ecological changes will all have an impact on fisheries and aquaculture as a result of climate change.This method used a bibliometric analysis using VoS Viewer and collected articles from the Scopus Database for the years 2014-2022.The results show that (1) the number of documents is rising, but the citations go down temporarily; (2) the countries with the most influence are the United States, United Kingdom, and Canada; (3) the authors most cited are Cheung, W.W.L., and Allison, E.H.; (4) the research most influenced is the livelihoods approach and management of small-scale fisheries (2001) by Allison, E.H., and Ellis, F. (5) The most frequently used keywords are climate change, fisheries, livelihood, human, animal, ecosystem, environmental protection, adaptive management, fishery management, conservation of natural resources, and fish.Therefore, future research related to fishery livelihood due to climate change should include several themes, including social-ecological systems, remote sensing, artisanal fisheries, the Pacific Islands, and coastal impact.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.137 | 0.173 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| 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".