{"id":"W2730746908","doi":"10.1016/j.jaap.2017.06.027","title":"Ultrasonic pretreatment effects on the bio-oil yield of a laboratory-scale slow wood pyrolysis","year":2017,"lang":"en","type":"article","venue":"Journal of Analytical and Applied Pyrolysis","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Pyrolysis; Lignocellulosic biomass; Pulp and paper industry; Yield (engineering); Biomass (ecology); Materials science; Pyrolysis oil; Bioenergy; Ultrasonic sensor; Biofuel; Environmental science; Chemical engineering; Chemistry; Waste management; Organic chemistry; Composite material; Acoustics; Engineering; Agronomy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002253461,0.0001933777,0.0004747272,0.0000979882,0.0001257506,0.00007941915,0.0003611204,0.0001154659,0.0001007717],"category_scores_gemma":[0.0001674979,0.0001147134,0.0002021459,0.0001906002,0.0001467841,0.00007682783,0.00004289931,0.0002454144,0.00001111563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003606014,"about_ca_system_score_gemma":0.00002162536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002257241,"about_ca_topic_score_gemma":0.000001333852,"domain_scores_codex":[0.998954,0.00001497815,0.0003577259,0.0001435732,0.0003319816,0.0001977589],"domain_scores_gemma":[0.9986281,0.0005220356,0.000241893,0.0003584983,0.00008372626,0.0001657107],"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":[0.0002673531,0.0002281362,0.001487719,0.0003026501,0.001714124,0.00002327818,0.000211641,0.0001481777,0.9662375,0.000873534,0.001560317,0.0269456],"study_design_scores_gemma":[0.0005620891,0.0001401945,0.00129571,0.0001221434,0.0006110838,0.000004076062,0.00009568824,0.0003080941,0.9961864,0.0002539921,0.0002730712,0.0001475097],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966028,0.0003752485,0.00005527361,0.0009926811,0.00005242431,0.00004532234,0.00001000705,0.00001791476,0.001848282],"genre_scores_gemma":[0.9993063,0.0003156913,0.0001100429,0.00007980908,0.00009229269,0.000004916283,4.984293e-7,0.00001627012,0.00007419755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02994888,"threshold_uncertainty_score":0.4677877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006875549752307677,"score_gpt":0.2015059340077308,"score_spread":0.1946303842554231,"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."}}