Bibliographic record
Abstract
In a world where marine pollution is increasing, marine life is also under threat. Although the world has started to protect marine life, it should start by reducing marine pollution. In order to explore the harm of marine pollution to marine life, this paper summarizes the sources of marine pollution, the harm to marine life and the measures to be taken. The main sources of marine pollution are agriculture, which uses pesticides; industry, which spills oil; tourism, which produces waste; and everyday life, which discharges waste water. Different sources of marine pollution alter the marine environment to different degrees. Among the marine organisms affected by marine pollution, some animals are bound and injured by solid pollutants such as plastics, and some plants are covered by substances that enter the ocean with liquid pollutants and the effects of ocean eutrophication. After investigation and research, a series of measures will be proposed to government agencies and scientific research departments to reduce marine pollution, and young people will be urged to raise their environmental awareness and protect marine organisms. It is hoped that through the effective measures taken by various departments, human beings will be able to provide a near-"pollution-free" marine environment for marine organisms in the future, so that marine organisms will no longer be endangered.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".