Detecting Plastic Pollution in Aquatic Environment Using Remote Sensing Technology: Cost-Saving Method in Pollution and Risk Management for Developing Countries
Bibliographic record
Abstract
One of the crucial elements that is directly tied to the quality of living organisms is the quality of the water. However, water quality has been adversely affected by plastic pollution, a global environmental disaster that has an effect on aquatic life, wildlife, and human health. To prevent these effects, better monitoring, detection, characterisation, quantification, and tracking of aquatic plastic pollution at regional and global scales is urgently needed. Remote sensing technology is regarded as a useful technique, as it offers a promising new and less labour-intensive tool for the detection, quantification, and characterisation of aquatic plastic pollution. The study seeks to supplement to the body of scientific literature by compiling original data on the monitoring of plastic pollution in aquatic environments using remote sensing technology, which can function as a cost saving method for water pollution and risk management in developing nations. This article provides a profound analysis of plastic pollution, including its categories, sources, distribution, chemical properties, and potential risks. It also provides an in-depth review of remote sensing technologies, satellite-derived indices, and research trends related to their applicability. Additionally, the study clarifies the difficulties in using remote sensing technologies for aquatic plastic monitoring and practical ways to reduce aquatic plastic pollution. The study will improve the understanding of aquatic plastic pollution, health hazards, and the suitability of remote sensing technology for aquatic plastic contamination monitoring studies among researchers and interested parties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".