{"id":"W4388102298","doi":"10.1021/acsomega.3c07228","title":"Prediction of Thermogravimetric Data in the Thermal Recycling of e-waste Using Machine Learning Techniques: A Data-driven Approach","year":2023,"lang":"en","type":"article","venue":"ACS Omega","topic":"Recycling and Waste Management Techniques","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"United Arab Emirates University; Advanced Technology Research Council","keywords":"Thermogravimetric analysis; Combustion; Hematite; Pyrolysis; Pyrolytic carbon; Work (physics); Thermal decomposition; Environmental science; Process engineering; Materials science; Waste management; Computer science; Chemistry; Chemical engineering; Engineering; Mechanical engineering; Organic chemistry; Metallurgy","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":[0.002592597,0.0001193195,0.0001895448,0.0002569088,0.00007760931,0.00002192284,0.001686627,0.00006647971,0.000006005746],"category_scores_gemma":[0.0001533043,0.00008696499,0.00002769489,0.001834031,0.0001199844,0.0003630431,0.00161785,0.000248009,0.000003864428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002724392,"about_ca_system_score_gemma":0.000005455319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001128198,"about_ca_topic_score_gemma":0.00001474262,"domain_scores_codex":[0.9983763,0.0002196102,0.0003795951,0.0003940817,0.000424516,0.0002059174],"domain_scores_gemma":[0.9982961,0.0001340944,0.0002296351,0.001316704,0.00000592084,0.00001757164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005690633,0.0004258926,0.6849208,0.000201087,0.00008900892,0.00001304239,0.001912164,0.03255247,0.1297089,0.00006063269,0.0008579195,0.1492012],"study_design_scores_gemma":[0.0003515444,0.0001810144,0.05960467,0.0001629905,0.0001011047,0.000006689097,0.001383259,0.9270343,0.008583064,0.0001896229,0.002149769,0.0002519828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9913712,0.00006316926,0.003265204,0.0000386978,0.00002944466,0.000441223,0.00007524371,0.0001674146,0.004548418],"genre_scores_gemma":[0.9894429,0.0001745538,0.009859463,0.00001623485,0.00003169883,0.00001073634,0.0003834703,0.00001927787,0.00006162145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8944818,"threshold_uncertainty_score":0.3546329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1094541294355459,"score_gpt":0.2998941318862271,"score_spread":0.1904400024506812,"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."}}