{"id":"W2766239411","doi":"10.1002/cjce.23060","title":"Kinetics of cellulose pyrolysis: Ensuring optimal outcomes","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Chemical, Bioengineering, Environmental, and Transport Systems","keywords":"Microcrystalline cellulose; Cellulose; Pyrolysis; Activation energy; Thermogravimetric analysis; Thermal decomposition; Decomposition; Biomass (ecology); Kinetics; Chemical engineering; Gravimetric analysis; Kinetic energy; Materials science; Thermodynamics; Chemistry; Organic chemistry; Physics; Engineering; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002420886,0.0009727463,0.0009584133,0.0005243509,0.0005354176,0.001856781,0.0005984756,0.0006101229,0.001947703],"category_scores_gemma":[0.003193533,0.0004307143,0.0003910552,0.0007460805,0.0003276374,0.001587049,0.0009871578,0.001252129,0.00124803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008168642,"about_ca_system_score_gemma":0.001085099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009340695,"about_ca_topic_score_gemma":0.00107072,"domain_scores_codex":[0.9988171,0.0001469833,0.0001068617,0.0002977253,0.000389883,0.0002416108],"domain_scores_gemma":[0.9992821,0.0002850267,0.0001193585,0.0001083648,0.000166156,0.00003900985],"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":[0.0008382519,0.0003816451,0.002870045,0.0006073713,0.00005360899,0.0002719362,0.0001372457,0.05796346,0.8820038,0.00400152,0.0008479551,0.05002309],"study_design_scores_gemma":[0.00001620082,0.0002958398,0.0009227114,0.00002961237,0.00001718166,0.00005211028,0.00005397023,0.04301333,0.9521996,0.001289095,0.002085508,0.00002486191],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8139149,0.005809967,0.1649268,0.0007493959,0.0001332691,0.0002468846,0.001119557,0.001110623,0.01198859],"genre_scores_gemma":[0.9704623,0.002031357,0.02541134,0.00005967639,0.00001888135,0.0001197043,0.0003935784,0.0002322009,0.001270981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002420886,"threshold_uncertainty_score":0.01280302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008625047162940174,"score_gpt":0.1877403536854316,"score_spread":0.1791153065224914,"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."}}