{"id":"W4386893747","doi":"10.21203/rs.3.rs-3362131/v1","title":"Feature selection for constructing datasets toward automated lifecycle assessment for additive manufacturing","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sustainability; Computer science; Process (computing); Key (lock); Selection (genetic algorithm); Life-cycle assessment; Product (mathematics); Scale (ratio); Production (economics); Supply chain; Process management; Fused filament fabrication; Systems engineering; Industrial engineering; Risk analysis (engineering); Engineering; Artificial intelligence; Business; Mathematics","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":[0.003263132,0.001233397,0.001038585,0.006650333,0.000726624,0.001963036,0.001851177,0.001664381,0.001975151],"category_scores_gemma":[0.01502956,0.0002917002,0.002003179,0.004339507,0.0005255295,0.001806324,0.001841052,0.001424775,0.001089507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009595219,"about_ca_system_score_gemma":0.001516621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003619497,"about_ca_topic_score_gemma":0.004734692,"domain_scores_codex":[0.9972253,0.0008466561,0.0002827564,0.0007456623,0.0006874488,0.0002121277],"domain_scores_gemma":[0.9915873,0.005044296,0.0006794296,0.001330355,0.001148551,0.0002100177],"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":[0.0008366062,0.001604629,0.07582995,0.001865664,0.0005926545,0.001202748,0.0003881497,0.226571,0.01039709,0.01098724,0.06920259,0.6005217],"study_design_scores_gemma":[0.0001189455,0.0003243042,0.02753442,0.0003154296,0.0001968939,0.0003902387,0.0005233672,0.8843429,0.01064056,0.02509308,0.05041203,0.0001078087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2498684,0.003089006,0.6075868,0.002222057,0.0004113681,0.001330857,0.1187532,0.01073172,0.006006581],"genre_scores_gemma":[0.4552118,0.0006346213,0.3517771,0.0002957057,0.0001610824,0.00191524,0.1886844,0.0001853699,0.001134741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006650333,"threshold_uncertainty_score":0.01725727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0772143165117411,"score_gpt":0.3972209320370747,"score_spread":0.3200066155253336,"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."}}