{"id":"W4385725458","doi":"10.1016/j.jece.2023.110732","title":"Machine learning-assisted selection of adsorption-based carbon dioxide capture materials","year":2023,"lang":"en","type":"article","venue":"Journal of environmental chemical engineering","topic":"Carbon Dioxide Capture Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Polytechnique Montréal","funders":"Office of Energy Research and Development","keywords":"Carbon dioxide; Selection (genetic algorithm); Adsorption; Environmental science; Computer science; Process engineering; Materials science; Chemistry; Artificial intelligence; Engineering; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009232057,0.0007767925,0.001053192,0.001529515,0.0004718627,0.0008375822,0.0008457659,0.0008379567,0.001537648],"category_scores_gemma":[0.00144919,0.0002610295,0.0008383924,0.0006690603,0.0002223051,0.0004400018,0.0003523674,0.000439281,0.0004475524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005928,"about_ca_system_score_gemma":0.000681041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001846653,"about_ca_topic_score_gemma":0.002789272,"domain_scores_codex":[0.9996777,0.00006938683,0.00001923421,0.00007355808,0.00009746796,0.00006279456],"domain_scores_gemma":[0.9994057,0.0002531675,0.00004447855,0.00003256191,0.0002317878,0.00003222982],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002380678,0.001212797,0.01355454,0.0005550691,0.0004120362,0.000345555,0.00006932196,0.2695282,0.3196531,0.001775026,0.004125696,0.3863879],"study_design_scores_gemma":[0.00004864464,0.0002132095,0.002119389,0.000005815916,0.00007632658,0.0000596779,0.00001567552,0.9271595,0.06932022,0.0002504137,0.0007140341,0.00001707112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8517907,0.001749254,0.1367018,0.0003142197,0.0001495064,0.0002406338,0.0005095649,0.002291584,0.006252866],"genre_scores_gemma":[0.9600475,0.0001958194,0.03733236,0.00008829722,0.00002702288,0.00007717597,0.000481996,0.00007915561,0.001670523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001846653,"threshold_uncertainty_score":0.00514394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004550240767654731,"score_gpt":0.1702847897047121,"score_spread":0.1657345489370573,"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."}}