The harm of difficult to degrade plastics to animals and the surrounding environment
Bibliographic record
Abstract
Littering or the disposal of plastic products and particles in the environment is a current global concern. Because plastics are long-lasting and are used extensively, productivity has increased dramatically to 400 million tons per year, and only a small fraction of them is recycled or disposed of correctly. This research aimed at examining the impacts of plastic pollution on soil and marine environment especially on the changes in physical and chemical properties of the soil, the effects on plant growth and marine organisms through ingestion and entanglement. The study also notes that microplastics have negative impacts on the physical characteristics of the soil including fertility and permeability as well as the behavior of macro-organisms like earthworms, and marine life is impacted by species entanglement and ingestion of plastics leading to extensive ecological risks. Furthermore, while biodegradable plastics are more environmentally friendly, they decompose at varying speeds depending on the conditions and microorganisms. The study also highlights the importance of improved waste management, reduction in the use of plastics, enhancement of recycling and biodegradation studies to minimize the adverse effects on the environment in the long run. This work assists in increasing the public’s understanding of the global problem of plastic pollution and promotes the need for a universal approach to minimizing the negative impact on the environment.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".