{"id":"W4405174917","doi":"10.1016/j.enconman.2024.119373","title":"Machine learning-driven optimization for sustainable CO2-to-methanol conversion through catalytic hydrogenation","year":2024,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University","funders":"Institute of Tropical Aquaculture and Fisheries, University Malaysia Terengganu; Universiti Sultan Zainal Abidin; University of Tehran; Ministry of Higher Education, Malaysia","keywords":"Methanol; Catalysis; Process engineering; Chemical engineering; Environmental science; Waste management; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001107265,0.000819006,0.001135213,0.0004934041,0.00027109,0.0008543528,0.0007001307,0.0009239624,0.001206775],"category_scores_gemma":[0.001776177,0.0005154373,0.000742005,0.0005565743,0.0004987593,0.0005618797,0.0006353384,0.0009310419,0.0002427401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001029682,"about_ca_system_score_gemma":0.001297588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005523112,"about_ca_topic_score_gemma":0.0045913,"domain_scores_codex":[0.9997651,0.00008247908,0.00001024227,0.00004544852,0.0000578186,0.00003890856],"domain_scores_gemma":[0.9993686,0.000434879,0.00005454628,0.00002485846,0.00009487962,0.00002231079],"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":[0.00001398033,0.00001520173,0.0001402361,0.00001967752,0.000007322077,0.00001048989,0.000003125734,0.9962144,0.0004594488,0.0003583747,0.00007470998,0.002683061],"study_design_scores_gemma":[0.000002088565,0.000007418354,0.00003139372,0.000001024392,0.000001148767,8.932822e-7,0.000001120416,0.9995306,0.000156674,0.0002005694,0.00006629346,9.315536e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3527652,0.002321946,0.6302366,0.0008859879,0.000128919,0.0001979883,0.000419063,0.0008973092,0.01214694],"genre_scores_gemma":[0.9446642,0.0004538415,0.05165848,0.0001012727,0.00002511556,0.0002009489,0.000286309,0.0000646799,0.002545137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005523112,"threshold_uncertainty_score":0.01098192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007584714382429592,"score_gpt":0.2387109891370395,"score_spread":0.2311262747546099,"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."}}