{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003054139,0.0005400175,0.0004819829,0.001237584,0.0003349594,0.00078917,0.0005859642,0.0009540074,0.002694887],"category_scores_gemma":[0.0003822524,0.0005121327,0.0007586957,0.0007568413,0.000364515,0.0004632827,0.0005126733,0.0008191948,0.002081636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005581375,"about_ca_system_score_gemma":0.0003547744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000386075,"about_ca_topic_score_gemma":0.0004164915,"domain_scores_codex":[0.9995042,0.00003197557,0.00002410169,0.00009180382,0.0003094712,0.00003848095],"domain_scores_gemma":[0.9997929,0.0000726525,0.00002677012,0.00005796983,0.00004034446,0.000009265686],"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":[0.0001033549,0.0000566953,0.0002967124,0.001001223,0.00002634402,0.0004452627,0.0001681373,0.01169205,0.7722372,0.01406727,0.003597012,0.1963087],"study_design_scores_gemma":[0.00002957032,0.0001719316,0.0008022015,0.00006562653,0.00002403605,0.00157306,0.00001570483,0.04224641,0.8519019,0.002835198,0.1002586,0.00007566775],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03612546,0.006406163,0.9313294,0.0001855996,0.0004554827,0.0001543235,0.0005683822,0.00266612,0.02210908],"genre_scores_gemma":[0.2458871,0.004939234,0.7363296,0.0001364287,0.0001187179,0.0002615299,0.0004462092,0.0001998705,0.0116814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002694887,"threshold_uncertainty_score":0.009015262,"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."}}