{"id":"W4405986911","doi":"10.1016/j.apenergy.2024.125258","title":"Zeolite-catalytic pyrolysis of waste plastics: Machine learning prediction, interpretation, and optimization","year":2025,"lang":"en","type":"article","venue":"Applied Energy","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Foundation of State Key Laboratory of Coal Combustion; Alliance of International Science Organizations; National Natural Science Foundation of China","keywords":"Zeolite; Interpretation (philosophy); Pyrolysis; Catalysis; Waste management; Process engineering; Chemical engineering; Engineering; Environmental science; Computer science; Chemistry; Organic chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000148447,0.00009934028,0.0001362493,0.0002206466,0.0001047321,0.00006177174,0.000239981,0.00005234952,0.000005706805],"category_scores_gemma":[0.00009192726,0.00009773635,0.00002066452,0.0004522088,0.00004283211,0.0001537149,0.0001419714,0.00009573197,0.000001698244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001917441,"about_ca_system_score_gemma":0.00003086947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001078094,"about_ca_topic_score_gemma":0.00001176659,"domain_scores_codex":[0.9991693,0.00004066229,0.0002660087,0.0002928677,0.000125543,0.0001056574],"domain_scores_gemma":[0.9993345,0.0001346315,0.000137549,0.0002891483,0.00006829279,0.00003594696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001711931,0.00002786972,0.0009709405,0.00003071298,0.00003463696,1.669546e-7,0.0002465736,0.6991071,0.0009104793,0.2501383,0.0001190538,0.04839715],"study_design_scores_gemma":[0.000240173,0.00002564861,0.0005670349,0.00002006877,0.00002280033,9.582888e-7,0.00003757957,0.993692,0.001886178,0.00118847,0.002248064,0.00007104816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001219631,0.0001450045,0.9920978,0.0001899972,0.00008258873,0.00003670059,0.000005281265,0.0001220065,0.006101019],"genre_scores_gemma":[0.9795603,0.00009920081,0.01953642,0.00008756639,0.00001987784,0.00001886314,0.0002060355,0.000006451487,0.0004652847],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9783407,"threshold_uncertainty_score":0.3985573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004258632305308585,"score_gpt":0.2000184934190471,"score_spread":0.1957598611137385,"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."}}