A bibliometrics study of plants, animals, bacteria, algae and technologies that reduce, filter and eliminate microplastics from planet earth, ecological solutions for the environment
Bibliographic record
Abstract
The world surrounded by plastics generates a lot of uncertainty and the first victims are sea animals, plastic in contact with the sun is able to disintegrate and generate toxins that are harmful to health. It is for this reason that this research in bibliographic review allows us to know the different solutions to counteract microplastics through the analysis of the Scopus database and the VOSviewer tool that allows us to analyze the data, considering the essential characteristics that are plants, animals, bacteria, algae and technologies that allow the disintegration, elimination and purification of microplastics, graphs and tables were obtained which allow us to recognize the analyzed data, the countries that carry out these investigations and the bibliometric maps worldwide. The results allow us to understand that the existence of microplastics generates many negative consequences for planet earth, however, there are different solutions which we can use and apply to counteract these microplastics, also considering that countries like Peru do not find published scientific research relevant to this matter. The purpose of this research is to allow us to make better decisions and not lose heart in the face of microplastics since it can be fought with the different solutions that we find on planet earth, technology and the other objective is to motivate readers to take action in the issue and allow generating change in the use of plastics.
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 | 0.006 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.176 | 0.327 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".