{"id":"W2063626370","doi":"10.1115/imece2014-37828","title":"Functional Prototyping and Tooling of FDM Additive Manufactured Parts","year":2014,"lang":"en","type":"article","venue":"Volume 2A: Advanced Manufacturing","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sheridan College","funders":"","keywords":"Rapid prototyping; CAD; Mechanical engineering; Computational fluid dynamics; Software; Computer Aided Design; Wind tunnel; Computer science; Engineering; Engineering drawing; Aerospace 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":[0.0001882973,0.0003928704,0.000443223,0.0003189433,0.000182184,0.00004655918,0.0002084055,0.0001469051,0.000130299],"category_scores_gemma":[0.0001278942,0.0003988615,0.00009773342,0.0001001493,0.0001629887,0.000321456,0.000174413,0.0004489782,0.0000283449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007065939,"about_ca_system_score_gemma":0.000008686456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000919071,"about_ca_topic_score_gemma":0.00001484128,"domain_scores_codex":[0.998337,0.00003348505,0.0004173951,0.0004567161,0.0002326864,0.0005226916],"domain_scores_gemma":[0.9991241,0.0002003193,0.0001405901,0.0003937998,0.00004418946,0.00009706106],"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":[0.0000836985,0.00004091677,0.0006072544,0.0007197706,0.0002090968,0.00001077328,0.0003807558,0.07950637,0.006762246,0.0009329389,0.001713529,0.9090326],"study_design_scores_gemma":[0.0005810555,0.00007777028,0.0269151,0.0002162216,0.00002646788,0.00001431399,0.000121893,0.006229277,0.908628,0.005071266,0.05163602,0.0004826511],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9394916,0.000261486,0.05322462,0.00007558893,0.0004724122,0.0005716783,0.00004014057,0.001785684,0.004076735],"genre_scores_gemma":[0.9905007,0.00009260708,0.008552133,0.000033757,0.0001512687,0.0001215115,0.00002995453,0.00007273231,0.0004453551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.90855,"threshold_uncertainty_score":0.9998463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008108336404187037,"score_gpt":0.1928030498957633,"score_spread":0.1846947134915763,"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."}}