{"id":"W2790674409","doi":"10.5380/rber.v7i2.58263","title":"EVALUATION OF THE ELEMENTAL COMPOSITION OF MUNICIPAL SOLID WASTE BOTTOM ASH: A NEW METHODOLOGY FOR SAMPLE PREPARATION","year":2018,"lang":"en","type":"article","venue":"Revista Brasileira de Energias Renováveis","topic":"Municipal Solid Waste Management","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Air (Canada)","funders":"Agência Nacional de Energia Elétrica; Petrobras; Fundação de Amparo à Pesquisa do Estado de São Paulo; Universidade Federal do ABC","keywords":"Bottom ash; Municipal solid waste; Elemental analysis; cardboard; Scanning electron microscope; Waste management; Organic matter; Environmental science; Energy-dispersive X-ray spectroscopy; Composition (language); Materials science; Pulp and paper industry; Chemistry; Fly ash; Engineering; Composite material","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003489689,0.0001880388,0.0003346702,0.00006467135,0.0001225091,0.00002526432,0.0005408273,0.00009737194,0.00126281],"category_scores_gemma":[0.000474129,0.0001583273,0.0001764747,0.0003806281,0.0002868616,0.0001436794,0.0003790951,0.00009021897,0.00001728961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004532834,"about_ca_system_score_gemma":0.00007849337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002219005,"about_ca_topic_score_gemma":0.0009358343,"domain_scores_codex":[0.9968486,0.001033574,0.0006450266,0.0003561492,0.0007980843,0.0003185583],"domain_scores_gemma":[0.9982054,0.00030249,0.0005720851,0.0007365378,0.0001023147,0.00008116318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001167985,0.0009541817,0.04224902,0.0003807331,0.0006658491,6.916183e-7,0.01993728,0.4911779,0.3624169,0.01666139,0.01725412,0.04713393],"study_design_scores_gemma":[0.003881751,0.001750221,0.04940785,0.0002974553,0.001541331,0.00001184025,0.001672613,0.6275606,0.3024103,0.002992643,0.007782605,0.0006907939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9642777,0.00008030803,0.03233904,0.00006080413,0.000146615,0.001143535,0.00004778701,0.00001314285,0.001890996],"genre_scores_gemma":[0.9756821,0.000008092845,0.02370341,0.0001097762,0.0001535032,0.00004806316,0.00004646837,0.00002359033,0.0002249775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1363827,"threshold_uncertainty_score":0.9996502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0980507447874911,"score_gpt":0.3748732400610248,"score_spread":0.2768224952735337,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}