{"id":"W4308908004","doi":"10.1007/s40964-022-00359-7","title":"Augmenting mechanical design engineering with additive manufacturing","year":2022,"lang":"en","type":"article","venue":"Progress in Additive Manufacturing","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Creativity; Product design; Engineering; Product (mathematics); New product development; Engineering design process; Process (computing); Manufacturing engineering; Computer science; Knowledge management; Mechanical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006333196,0.000796292,0.0006518713,0.0008133214,0.0005178991,0.0001535099,0.0008703847,0.0001602379,0.0006588962],"category_scores_gemma":[0.00005836519,0.0008164759,0.000147567,0.0003245984,0.0001635804,0.0003821743,0.0009095963,0.001942172,0.00003314601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007750239,"about_ca_system_score_gemma":0.00003582935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001099084,"about_ca_topic_score_gemma":0.00000739355,"domain_scores_codex":[0.9961368,0.0001650733,0.0006172764,0.0009313836,0.000736159,0.001413309],"domain_scores_gemma":[0.9984909,0.0005638304,0.0001815596,0.000572652,0.00003220308,0.0001588387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000330152,0.0002932899,0.0003975056,0.0003412699,0.0007101787,0.001892454,0.001183918,0.2977571,0.0003051588,0.001188163,0.001484522,0.6941163],"study_design_scores_gemma":[0.001186088,0.0002644942,0.005065848,0.0002555672,0.00004657299,0.0001521839,0.0008014856,0.02088937,0.9583667,0.0007521546,0.01090788,0.001311677],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9156318,0.0005804363,0.0722863,0.0001490522,0.001000373,0.001872043,0.0003799018,0.006300543,0.001799599],"genre_scores_gemma":[0.9806168,0.00005190198,0.01711198,0.00004047332,0.0001513549,0.001657784,0.00009636534,0.000223397,0.00004995423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9580615,"threshold_uncertainty_score":0.9994286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01357903314323086,"score_gpt":0.2130935333637134,"score_spread":0.1995145002204826,"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."}}