{"id":"W2966092124","doi":"10.1017/dsi.2019.72","title":"Automated Candidate Detection for Additive Manufacturing: A Framework Proposal","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ... International Conference on Engineering Design","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Set (abstract data type); Product (mathematics); Risk analysis (engineering); Resource (disambiguation); Selection (genetic algorithm); Tacit knowledge; Process management; Machine learning; Knowledge management; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006850925,0.001024312,0.001145394,0.006193148,0.001366222,0.005541389,0.003444362,0.001996184,0.003368634],"category_scores_gemma":[0.008402721,0.0006401828,0.003236492,0.003660168,0.001955469,0.003193823,0.003359691,0.001337162,0.001082903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002351622,"about_ca_system_score_gemma":0.00432031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01560853,"about_ca_topic_score_gemma":0.01336437,"domain_scores_codex":[0.9960048,0.001291061,0.0003674093,0.0007354125,0.0013045,0.0002969035],"domain_scores_gemma":[0.9950767,0.00267088,0.0003604164,0.0003864644,0.001247771,0.0002578515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000224136,0.0005714527,0.007577166,0.001202593,0.0004389455,0.001907005,0.001236263,0.2067969,0.01018909,0.4336296,0.01051351,0.3257132],"study_design_scores_gemma":[0.00002835801,0.00008385879,0.000654649,0.0002220733,0.0001022384,0.0004095338,0.0003426671,0.8984133,0.00318614,0.08060196,0.0159021,0.00005314669],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002691563,0.0003524075,0.9927556,0.0005207349,0.00002584955,0.0002977595,0.0002579627,0.0009881809,0.002109859],"genre_scores_gemma":[0.09187816,0.0004230681,0.9050937,0.000123879,0.00004519229,0.0003550346,0.0006689113,0.00007496522,0.001337105],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01560853,"threshold_uncertainty_score":0.03623158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01684116378230809,"score_gpt":0.2282472644176431,"score_spread":0.211406100635335,"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."}}