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Record W4390719965 · doi:10.11109/jaes.2023.25.1.017

Estimation of biodegradable-resin degradation via controlled composting

2023· article· en· W4390719965 on OpenAlexaff
Seunggun Won, Anthony Lau

Bibliographic record

VenueJournal of Animal Environmental Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiodegradationDegradation (telecommunications)BioplasticInertCellulosePulp and paper industryWaste managementEnvironmental scienceMaterials scienceChemistryOrganic chemistryComputer scienceEngineering

Abstract

fetched live from OpenAlex

The improvement of life style has resulted in the increment of outdoor activities and leisure, which caused the increase of disposable products due to its convenience. Petroleum based plastics have shown the negative impacts on the environment with their inert characteristics. In order to improve such a situation, many studies have suggested biodegradable materials for disposable products. However, the determination of biodegradability is not very straightforward and for its convenience, short period and reliance of biodegradability shall be proved. The composting is one way to test biodegradability via American society for testing and materials (ASTM) with ASTM 5338 and 6400. Two different PLA bioplastic resins were tested in temperature controlled composting system in this study in which the cellulose as reference was used for comparison. For 100 days, the degradability of 68.4% in cellulose was achieved and testing materials of TF and XD showed 54.0 and 44.0%, respectively. The degradation analysis via CO2 capture showed the similar trend of biodegradability in weight basis but the values were difference, which may be some technical errors which shall be improved in the future. Through the model equations, the complete degradation period may be predicted and the values will be reliable with many repeated trials.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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 designBench or experimental
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

Citations2
Published2023
Admission routes1
Has abstractyes

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