{"id":"W4213230479","doi":"10.29327/aconemi.389207","title":"USO DE CAD/CAM, NANOTECNOLOGIA, MÉTODO DE ELEMENTOS FINITOS, INTENET OF THINGS E IMPRESSÃO 3D NO DESENVOLVIMENTO DE UM DISPOSITIVO DE MEDIÇÃO DE FORÇA DE MORDIDA DE BAIXO CUSTO.","year":2022,"lang":"pt","type":"article","venue":"Anais do Congresso Internacional de Engenharia Mecânica e Industrial","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Air (Canada)","funders":"","keywords":"Humanities; Art","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","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.004928353,0.00121846,0.001331125,0.0006876696,0.0009221769,0.0004694753,0.003980312,0.001877,0.002387256],"category_scores_gemma":[0.00790129,0.001404649,0.0007575279,0.0003591318,0.0006146705,0.0004536473,0.003103331,0.005637573,0.00005179372],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005873735,"about_ca_system_score_gemma":0.002228442,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008560152,"about_ca_topic_score_gemma":0.0004260437,"domain_scores_codex":[0.9911848,0.001552152,0.001598468,0.001243559,0.001012043,0.003408947],"domain_scores_gemma":[0.9943141,0.002087868,0.00110148,0.001394422,0.0003007875,0.0008013196],"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":[0.01082233,0.002917463,0.2098563,0.00136445,0.01857624,0.006992456,0.02947922,0.07974777,0.3311154,0.0163179,0.1891668,0.1036437],"study_design_scores_gemma":[0.009444796,0.001892331,0.01016535,0.002443568,0.001852347,0.001190503,0.005159555,0.1235098,0.777073,0.006093324,0.05795638,0.003219081],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9626192,0.001476004,0.02825245,0.001476749,0.002257337,0.0008669547,0.001479125,0.0008057131,0.000766461],"genre_scores_gemma":[0.9872707,0.0006761862,0.008110889,0.0004498309,0.00143683,0.0005539184,0.0001605255,0.0002721701,0.001068975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4459576,"threshold_uncertainty_score":0.9994188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02557887361651925,"score_gpt":0.2602478061939712,"score_spread":0.2346689325774519,"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."}}