CHARACTERISATION OF MACRO-PLASTIC WASTE ALONG THE PHILIPPINES' LONGEST COASTAL BOULEVARD: BASIS FOR SOLID WASTE MANAGEMENT AND POLICY FORMULATION
Bibliographic record
Abstract
This study focused on the characterisation of macro-plastic waste in terms of types and quantities along the Philippines' longest coastal boulevard, considering density and cleanliness as a basis for solid waste management and policy formulation. Macro-plastic identification and counting were conducted in four (4) municipalities and eight (8) barangays using the standing stock survey method. There were six (6) types of macro-plastic waste, with the highest quantity consisting of beverage (37.2%) and food packaging (30.5%), followed by fishing gear (17.0%), plastic utensils (9.2%), toiletries (4.8%) and household wastes (1.4%). The computed overall density from a total of 3,978 macro-plastic waste items over 12,000 m2 of beach area sampled was 0.3 (CM), meaning there were 0.3 litter items of plastic per m2 throughout the whole boulevard. The computed overall beach cleanliness was 6.6, which means that the cleanliness status is moderate. Overall, most macro-plastic waste is generated by locals or visitors. In conclusion, a policy regulating the use, littering, and carrying of plastic along boulevards is recommended to prevent plastic pollution considering the province's growing ecotourism and the future expansion of the coastal boulevard.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".