{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008976707,0.0007886266,0.0003435333,0.001376837,0.0004009714,0.0004933936,0.000379649,0.0005345069,0.001080191],"category_scores_gemma":[0.0006415298,0.0002776654,0.000324225,0.0006742374,0.000408507,0.000440575,0.0004935271,0.0004602102,0.0005034788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002530652,"about_ca_system_score_gemma":0.0003588393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006502818,"about_ca_topic_score_gemma":0.003885291,"domain_scores_codex":[0.9989647,0.0001216667,0.0001061218,0.0002061549,0.000559797,0.00004157359],"domain_scores_gemma":[0.9996135,0.00006742025,0.00006323076,0.00005088405,0.0001902847,0.0000147276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004214047,0.00002570906,0.001687935,0.0001581865,0.00001579031,0.00005920957,0.00008690247,0.0001451654,0.9861984,0.0001173397,0.00004424437,0.01141899],"study_design_scores_gemma":[0.000007010819,0.000209536,0.009790283,0.00002551222,0.00005020439,0.0003694259,0.0001258014,0.001520751,0.9819406,0.0001325897,0.005812791,0.00001545185],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6921126,0.002878443,0.2967781,0.0001635402,0.000194976,0.0006034165,0.001319366,0.000472547,0.00547691],"genre_scores_gemma":[0.6309902,0.003344832,0.3577701,0.000118289,0.00005278936,0.0007277187,0.001234896,0.000168978,0.00559215],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001376837,"threshold_uncertainty_score":0.004747391,"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."}}