{"id":"W4387762819","doi":"10.1016/j.jmsy.2023.10.009","title":"Automating life cycle assessment for additive manufacturing with machine learning: Framework design, dataset buildup, and a case study","year":2023,"lang":"en","type":"article","venue":"Journal of Manufacturing Systems","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Life-cycle assessment; Process (computing); Computer science; Environmental impact assessment; Boosting (machine learning); Set (abstract data type); Industrial engineering; Machine learning; Systems engineering; Engineering; Artificial intelligence; Manufacturing engineering; Production (economics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001519754,0.0004597706,0.0007354815,0.0005610863,0.0004305675,0.0003167585,0.0003541851,0.0001692405,0.000010145],"category_scores_gemma":[0.0002869236,0.0003625258,0.00009087945,0.0001326304,0.00006539837,0.0003262768,0.0001952844,0.001079972,0.000005493324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001434241,"about_ca_system_score_gemma":0.00004340117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007218832,"about_ca_topic_score_gemma":0.000008809382,"domain_scores_codex":[0.9976211,0.0001930763,0.0008156277,0.0003582989,0.000448431,0.000563495],"domain_scores_gemma":[0.9972628,0.001552625,0.0005475322,0.0003621105,0.00007143117,0.0002034804],"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":[0.000135134,0.000134623,0.001186212,0.001118067,0.001668168,0.006483235,0.001977038,0.9519601,0.00007043447,0.0000264965,0.00231365,0.03292684],"study_design_scores_gemma":[0.01278069,0.009160528,0.04582528,0.006305172,0.001763768,0.03436403,0.08782633,0.615658,0.1621179,0.001610776,0.01754326,0.005044327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8936819,0.0001846211,0.103645,0.0000523229,0.0004390634,0.0008554982,0.0002155052,0.0009085257,0.00001756937],"genre_scores_gemma":[0.9865509,0.0000662157,0.01280984,0.000009802096,0.000284157,0.0001014855,0.00004402853,0.000110523,0.00002307144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3363021,"threshold_uncertainty_score":0.9998827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02647271580743165,"score_gpt":0.2801476288206435,"score_spread":0.2536749130132118,"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."}}