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Record W4317930059 · doi:10.26511/jkset.23.6.13

Physico-Chemical Characteristics of MSWs into Incinerators Before and After the COVID19 Pandemic

2022· article· en· W4317930059 on OpenAlexaboutno aff
Chun-Sik Lee, Jae Yong Ryu

Bibliographic record

VenueJournal of the Korean Society for Environmental Technology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsIncinerationQuarter (Canadian coin)PandemicWaste managementEnvironmental scienceMunicipal solid wasteCoronavirus disease 2019 (COVID-19)Heat of combustionWater contentGeographyEngineeringChemistryMedicineCombustion

Abstract

fetched live from OpenAlex

For 8 years before and after COVID19 in H-gun, a small and medium-sized rural city, a quarterly survey and analysis was conducted on changes in physical and chemical characteristics of domestic waste brought into the domestic waste incinerator input. Apparent density, three-component, and elemental analysis were performed, and based on this, the calorific value of waste to be recycled was calculated. Through this, the following results were obtained about the characteristics of household waste generation in rural small and medium-sized cities before and after the COVID19 pandemic. First, since the COVID19 pandemic in the fourth quarter of 2019, the content of plastics and fibers has increased relatively, and this reason is judged to be the result of the increase in the use of plastic packaging materials and masks. Second, in the case of moisture content and combustible content before and after COVID19, a decrease in moisture content and an increase in combustible content were evident overall, except for the quarter of a specific year. Third, comparing the low calorific value before and after COVID19, it was higher than about 3,000 kcal/kg from the fourth quarter of 2019, except for 2016, after the COVID19 pandemic.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.004
GPT teacher head0.200
Teacher spread0.197 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

Explore more

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