Gujarat’s plastic plight: unveiling characterization, abundance, and pollution index of beachside plastic pollution
Bibliographic record
Abstract
Abstract Plastic pollution poses a pervasive threat to ecosystems worldwide, jeopardizing marine life, contaminating water sources, and perpetuating a global environmental crisis. Spatial and temporal distribution of beach debris was quantitatively assessed on three recreational beaches in Gujarat State, India. A total of six debris categories were recorded with a mean of 0.9 items/m 2 in number and 3.62 g/m 2 in weight. A total of Mean debris concentrations and weight per debris item did not vary significantly between study sites. Highest debris concentrations were observed in October 2021 at all sites. Around 90% was macro-debris (2.5–100 cm), with white and transparent colours most frequently encountered. Based on Clean Coast Index findings, all sites were categorized as " dirty ". Plastic Abundance Index revealed that all sites had a very high abundance of plastics compared to other beach debris. Recreational activities on beaches, tourism, and extensive fishing can be the possible source of marine debris on Gujarat State. The findings of the current investigation is vital to understanding its pervasive environmental impact, encompassing threats to biodiversity, water quality, and ecosystems, while guiding effective policies to mitigate these repercussions on a global scale. It can be helpful to establish mitigation strategies urgently required to reduce marine debris pollution along the Gujarat Coast. It is recomanded to implement urgently needed mitigation strategies to diminish marine debris pollution along the Gujarat Coast.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".