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Record W2785070307 · doi:10.5380/biofix.v3i1.56038

COMPARAÇÃO ENTRE METODOLOGIAS USEPA E IPCC PARA ESTIMATIVA TEÓRICA DE PRODUÇÃO DE BIOGÁS EM ATERRO MUNICIPAL

2017· article· pt· W2785070307 on OpenAlexaff
Julia Bianek, Waldir Nagel Schirmer, Alexandre R. Cabral, Cléverson Luiz Dias Mayer, Pedro Henrique Mildemberger Eurich, Eduardo Henrique Martins

Bibliographic record

VenueBIOFIX Scientific Journal · 2017
Typearticle
Languagept
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPhysicsHumanitiesEnvironmental sciencePhilosophy

Abstract

fetched live from OpenAlex

O aproveitamento energético do biogás de aterros sanitários é uma alternativa sustentável para a disposição final de gases residuais. A estimativa teórica da produção desse biogás torna-se uma ferramenta útil para a avaliação de viabilidade e dimensionamento de sistemas de coleta de gás. Os modelos de geração de biogás de aterro mais utilizados atualmente são os baseados em equações de decaimento de primeira ordem. Os modelos LandGEM e IPCC foram utilizados neste trabalho para a estimativa da geração de gases no aterro municipal de Guarapuava (PR, Brasil), onde os principais parâmetros foram escolhidos com base nos dados frequentemente aplicados para cada modelo: valores default sugeridos para as condições locais específicas, para a aplicação do software LandGEM; e cálculo do potencial de geração de metano (L0) a partir da análise gravimétrica dos resíduos, para a aplicação da equação sugerida pelo IPCC. Estimou-se uma produção total de biogás de 44.466.711 Nm3.ano-1 aplicando LandGEM e 60.080.906 Nm3.ano-1 aplicando IPCC, onde os picos de produção foram observados nos anos de 2021 e 2020, respectivamente. A escolha do modelo mais adequado dependerá dos dados disponíveis e, para maior aproximação de resultados reais, os parâmetros adotados devem ser analisados com cautela, considerando as diferentes condições climáticas e operacionais de cada caso.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0070.003
Scholarly communication0.0070.001
Open science0.0060.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.004

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 head0.112
GPT teacher head0.348
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

Study designSimulation or modeling
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

Citations5
Published2017
Admission routes1
Has abstractyes

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