The human impact of marine ecosystem imbalance: an analysis of society and ocean management
Bibliographic record
Abstract
Throughout world history, truly great nations have had to find their place in the oceans. Over 40% of the global population and most of the world's megacities are located in coastal areas. The development of proper ocean management and the implementation of marine resource construction are the core strategies for national development on the world stage. Due to the increasing needs of national development, the oceans have gradually become ecologically unbalanced after enduring human exploitation. These ecological imbalances, such as marine plastic pollution, fish overfishing, and increased climate change, will eventually return to humans and pose a serious threat to human life and health. This paper discusses the desirable and undesirable interactions between the oceans and human health and the social structure of marine resource management. Besides, this paper will propose solutions to alleviate marine ecological problems as well as promote sustainable social development based on two structural analyses. The paper concludes that the relationship between human oceans should be mutually beneficial. Oceans desire to be managed and regulated by humans for their ecosystems and do not desire to be over-exploited and polluted. Humans' desire to derive resources from the oceans to meet their spiritual and health needs while not being exposed to the risks of disasters Analysis of the socio-ecological system should be used to determine options for reducing marine ecological issues while fostering sustainable social development (SES). To put it briefly, we can now strengthen policies that prioritize the health of the ocean and people, develop trustworthy relationships among stakeholders, and encourage economic incentives that alter behavior.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".