{"id":"W2173979487","doi":"10.1016/j.wasman.2015.11.018","title":"Sample preparation for thermo-gravimetric determination and thermo-gravimetric characterization of refuse derived fuel","year":2015,"lang":"en","type":"article","venue":"Waste Management","topic":"Iron and Steelmaking Processes","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gravimetric analysis; Thermogravimetric analysis; Refuse-derived fuel; RDF; Characterization (materials science); Materials science; Process engineering; Waste management; Chemistry; Computer science; Nanotechnology; Engineering; Organic chemistry; Waste treatment","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004254315,0.0008702716,0.0008997799,0.001114439,0.001261128,0.0003010468,0.0008691475,0.0007542177,0.00717521],"category_scores_gemma":[0.0008103951,0.0004086397,0.0004594055,0.001038227,0.0004530624,0.0004228689,0.0002912248,0.0009068042,0.002422831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002667176,"about_ca_system_score_gemma":0.0006613847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001276647,"about_ca_topic_score_gemma":0.005217012,"domain_scores_codex":[0.9995705,0.00003148285,0.00003413891,0.0001079143,0.000175258,0.00008054801],"domain_scores_gemma":[0.9996556,0.00005800168,0.00002612586,0.00006420587,0.0001750486,0.00002109064],"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":[0.0002292439,0.0001550219,0.0006375251,0.0002511487,0.00001584455,0.0001642392,0.0001184811,0.0003740486,0.9883524,0.0003772124,0.0004149946,0.008909871],"study_design_scores_gemma":[0.00003003131,0.0003336723,0.003159,0.0000335836,0.00002757413,0.0001960795,0.0001302335,0.001742952,0.9801075,0.0003476993,0.01386665,0.00002494736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8037354,0.001831377,0.1680881,0.0002942759,0.0003378301,0.003943198,0.00970022,0.001615075,0.01045461],"genre_scores_gemma":[0.7750975,0.003241831,0.1861137,0.0006872069,0.0002044173,0.007295327,0.01269493,0.0006730744,0.01399197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00717521,"threshold_uncertainty_score":0.02400345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02309867863667251,"score_gpt":0.2489582723755946,"score_spread":0.2258595937389221,"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."}}