{"id":"W2818070816","doi":"10.1016/j.jece.2018.07.012","title":"Kinetic parameter evaluation of groundnut shell pyrolysis through use of thermogravimetric analysis","year":2018,"lang":"en","type":"article","venue":"Journal of environmental chemical engineering","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Thermogravimetric analysis; Pyrolysis; Kinetic energy; Biofuel; Activation energy; Process (computing); Order of reaction; Shell (structure); Chemistry; Materials science; Chemical engineering; Kinetics; Organic chemistry; Waste management; Computer science; Reaction rate constant; Physics; 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":[0.0002977801,0.0003722844,0.0002963564,0.0005107066,0.0001818804,0.0003985499,0.0002304634,0.0002611074,0.0006486239],"category_scores_gemma":[0.0003204207,0.000182957,0.0004598353,0.0006057413,0.0001365617,0.0003208435,0.0001491809,0.0002564887,0.0001999261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002358523,"about_ca_system_score_gemma":0.000195393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002595822,"about_ca_topic_score_gemma":0.003872581,"domain_scores_codex":[0.9998212,0.00001420297,0.00001997206,0.00003779917,0.000090917,0.00001576583],"domain_scores_gemma":[0.9998591,0.00004071767,0.00002243988,0.00001160285,0.00005989541,0.000006147683],"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":[0.0002002261,0.00003405495,0.003514013,0.00007317534,0.00002317492,0.00004822372,0.00004276013,0.001936423,0.9871266,0.00007408255,0.00003586596,0.006891359],"study_design_scores_gemma":[0.000003345702,0.0001096483,0.01472584,0.000005359832,0.00003046827,0.00005160233,0.00004162175,0.009765413,0.9748005,0.00003427512,0.000418511,0.00001336454],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919317,0.0004333062,0.006184783,0.00001447777,0.00001024818,0.00001827193,0.0003766914,0.00004758346,0.0009828237],"genre_scores_gemma":[0.996029,0.0002855893,0.002549118,0.000005696736,0.000001456541,0.000009411653,0.0002554439,0.00001605072,0.0008483063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002595822,"threshold_uncertainty_score":0.005161464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0176614709960479,"score_gpt":0.214480452298315,"score_spread":0.1968189813022671,"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."}}