Identifying opportunities for harmonized microplastics and mesoplastics monitoring for Caribbean Small Island Developing States using a spatiotemporal assessment of beaches in South Eleuthera, The Bahamas
Bibliographic record
Abstract
Increasing quantities of microplastics and mesoplastics in the marine environment underscore the need for marine microplastics to be included in the global Plastics Treaty to end plastic pollution. Caribbean Small Island Developing States (SIDS) lack harmonized microplastics monitoring protocols, leaving them data deficient at the science-policy interface required for treaty negotiations. This baseline study assessed spatial and seasonal abundance and distribution of microplastic (1-5 mm) and mesoplastic (5-25 mm) on 16 beaches with three coastal exposures (Atlantic Ocean, Exuma Sound, Bahama Bank) in South Eleuthera, The Bahamas and its implications for Caribbean SIDS. Microplastics were the dominant debris type sampled (74 %) across all beaches, with significant spatial (p = 0.0005) and seasonal (p = 0.0363) differences in abundance and distribution across study sites. This baseline study identifies opportunities required for developing harmonized microplastics and mesoplastics monitoring by Caribbean SIDS to collect data to help support global plastics treaty negotiations.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".