Climate Change, Ocean Pollution, and Acidification: The Application of Integrated Management Strategies within the Framework of the United Nations Decade of Ocean Science
Bibliographic record
Abstract
As globalization accelerates, issues of climate change, ocean pollution, and acidification have become increasingly prominent, posing significant threats to marine ecosystems and human society. The United Nations Decade of Ocean Science for Sustainable Development (2021~2030) was initiated in response, aiming to strengthen scientific research and technological innovation to explore integrated management strategies to address these challenges. This study, framed within this context, delves into the impacts of climate change, ocean pollution, and acidification on ocean health and discusses comprehensive management strategies to mitigate these impacts, including but not limited to the advancement of scientific research, the application of technological innovations, and the development of international policies and management actions. Through case studies, this research aims to demonstrate the practices and effects of implementing these strategies globally, while analyzing the difficulties and challenges encountered in the process. Suggestions for future scientific research directions and the deepening of policies and cooperation are also proposed. This study underscores the importance of integrated management strategies in the global recovery of ocean health, intending to provide references and insights for future ocean science research and policy formulation.
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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| 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".