{"id":"W2903231397","doi":"10.1051/matecconf/201823702006","title":"Polymer Composite Manufacturing by FDM 3D Printing Technology","year":2018,"lang":"en","type":"article","venue":"MATEC Web of Conferences","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Materials Research; Independent Electricity System Operator; Uniwersytet Szczeciński","keywords":"3D printing; Fused deposition modeling; Composite number; 3d printed; Materials science; Manufacturing engineering; Mechanical engineering; Engineering drawing; Computer science; Engineering; Composite material","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000100638,0.0002162946,0.0002955141,0.0002817751,0.00009696056,0.00004263726,0.0004666882,0.0001847574,0.0003458001],"category_scores_gemma":[0.00002446788,0.0001983072,0.00004125004,0.0001339016,0.0004642369,0.0000738573,0.0001899544,0.0002314017,0.00006342518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001620827,"about_ca_system_score_gemma":0.00003039579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003506467,"about_ca_topic_score_gemma":0.00000772505,"domain_scores_codex":[0.998989,0.00001193161,0.000286395,0.0002252762,0.0001381706,0.0003491528],"domain_scores_gemma":[0.9994722,0.00005228329,0.00009140243,0.0003020416,0.00005065996,0.00003142117],"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":[0.00003064612,0.00008452342,0.0214585,0.000440708,0.0004305418,0.00001339642,0.0003029006,0.0001057926,0.4609876,0.01510516,0.005431944,0.4956083],"study_design_scores_gemma":[0.0001250872,0.0000511821,0.001446504,0.00009941547,0.00001119413,0.000005655929,0.0001582433,0.0006763778,0.9742352,0.000962291,0.02200718,0.0002216826],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9550198,0.0002764504,0.002184227,0.0001853209,0.000233969,0.00008629422,0.00001807513,0.001883606,0.0401122],"genre_scores_gemma":[0.9976031,0.00004989124,0.002004934,0.00001060857,0.000047438,0.00001252135,0.000005356743,0.00002343532,0.0002427476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5132476,"threshold_uncertainty_score":0.8086731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009755153456983235,"score_gpt":0.2148902357450851,"score_spread":0.2051350822881019,"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."}}