{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001607992,0.000211925,0.0001779866,0.0001663337,0.00004150969,0.00006999988,0.0005868305,0.0001302051,0.00004191433],"category_scores_gemma":[0.0002223769,0.0001729528,0.00008601438,0.0000827623,0.00003074277,0.0001267031,0.00007868897,0.0003065535,0.00001861074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001190423,"about_ca_system_score_gemma":0.00001745735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002793768,"about_ca_topic_score_gemma":2.28757e-7,"domain_scores_codex":[0.9991141,0.000002494985,0.0002053193,0.0002191352,0.000233653,0.0002252747],"domain_scores_gemma":[0.9994704,0.0001283366,0.00009574819,0.0001140919,0.0001628621,0.0000285585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004395103,0.0001134882,0.0001860993,0.0009140488,0.001009314,0.000001513597,0.000710748,0.1962133,0.5437375,0.2272681,0.005335666,0.02407078],"study_design_scores_gemma":[0.0001455134,0.00007088191,0.0004702717,0.0002483002,0.000009476968,0.00000309131,0.00004979318,0.2909003,0.7030782,0.004379477,0.0004898944,0.0001548141],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.802239,0.00001580948,0.1811194,0.0006208732,0.002965273,0.001790976,0.0001239705,0.005120514,0.006004222],"genre_scores_gemma":[0.9867564,0.00001203407,0.01283651,0.00001223912,0.00006956932,0.0001223329,0.000003435672,0.00004086704,0.0001465646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2228886,"threshold_uncertainty_score":0.7052811,"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."}}