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Record W4405388732 · doi:10.62051/s74xvx42

The harm of difficult to degrade plastics to animals and the surrounding environment

2024· article· en· W4405388732 on OpenAlexaff
Shijiu Gu

Bibliographic record

VenueTransactions on Environment Energy and Earth Sciences · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHarmForensic engineeringBusinessEnvironmental scienceEngineeringPsychologySocial psychology

Abstract

fetched live from OpenAlex

Littering or the disposal of plastic products and particles in the environment is a current global concern. Because plastics are long-lasting and are used extensively, productivity has increased dramatically to 400 million tons per year, and only a small fraction of them is recycled or disposed of correctly. This research aimed at examining the impacts of plastic pollution on soil and marine environment especially on the changes in physical and chemical properties of the soil, the effects on plant growth and marine organisms through ingestion and entanglement. The study also notes that microplastics have negative impacts on the physical characteristics of the soil including fertility and permeability as well as the behavior of macro-organisms like earthworms, and marine life is impacted by species entanglement and ingestion of plastics leading to extensive ecological risks. Furthermore, while biodegradable plastics are more environmentally friendly, they decompose at varying speeds depending on the conditions and microorganisms. The study also highlights the importance of improved waste management, reduction in the use of plastics, enhancement of recycling and biodegradation studies to minimize the adverse effects on the environment in the long run. This work assists in increasing the public’s understanding of the global problem of plastic pollution and promotes the need for a universal approach to minimizing the negative impact on the environment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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 designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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