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Record W4394010393 · doi:10.31357/ait.v3i2.7337

Present Status of Microplastic Pollution Research Data in Sri Lanka and Microplastic Risk Mitigation Solutions; Lessons from a Global Policy Context

2024· article· en· W4394010393 on OpenAlexaff
M.M.J.G.C.N. Jayasiri, Pradeep Gajanayake, Sajani H. Kolambage, Dasuni T. Bandaranayaka, Anushi Wijethunga, Danushika C. Manatunga, Amila Abeynayaka

Bibliographic record

VenueAdvances in Technology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSri lankaContext (archaeology)Environmental planningPollutionEnvironmental sciencePlastic pollutionBusinessMicroplasticsEnvironmental resource managementEnvironmental protectionGeographyOceanographyGeologyBiology

Abstract

fetched live from OpenAlex

The emergence of microplastics (MP) as a pollutant in natural environments including aquatics has been increasingly recognized worldwide. This review focuses on the status of MP pollution research in Sri Lanka, and MP risk mitigation solutions, as lessons from a global MP policy context. The methodology involves a comprehensive literature review divided into three main sections: 1) a simple understanding of the plastic cycle and risk factors,2) a comprehensive review of MP pollution research trends in Sri Lanka, 3) a comprehension of global trends of MP regulation policies and adaptable solutions for national scale. There was less attention given to MP research in Sri Lanka, until the recent X-Press Pearl disastrous incident. In addition to that, we highlight the less attention paid to MP pollution in inland waters and lands compared to marine. Considering the widespread MP issue, the paper highlights the importance of a policy approach for MP pollution control. Finally, the paper discusses the future directions for MP pollution research in Sri Lanka and emphasizes the need for more detailed quantitative data for effective policy formulation. The overall study presents a sound case for understanding a national context in MP pollution and suggesting necessary policy instruments in pollution regulation.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.349
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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