{"id":"W3037146998","doi":"10.3390/mi11070633","title":"Path Planning Strategies to Optimize Accuracy, Quality, Build Time and Material Use in Additive Manufacturing: A Review","year":2020,"lang":"en","type":"review","venue":"Micromachines","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":272,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Path (computing); Process (computing); Computer science; Subtractive color; Quality (philosophy); Benchmark (surveying); Industrial engineering; Manufacturing engineering; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002494431,0.0008995599,0.002415811,0.0003017466,0.00007212356,0.0003404948,0.0005695862,0.000336477,0.00009398821],"category_scores_gemma":[0.0003654875,0.0007616393,0.0002314993,0.0002033757,0.00007126615,0.0002606019,0.0006622425,0.0007896895,0.0001044352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001038033,"about_ca_system_score_gemma":0.00004822575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008167658,"about_ca_topic_score_gemma":0.000007396972,"domain_scores_codex":[0.9975802,0.0001863898,0.0008929002,0.000692633,0.0001571921,0.0004906992],"domain_scores_gemma":[0.9985785,0.0006073122,0.0002233626,0.0004544352,0.00001830828,0.0001180697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001268018,0.00001765567,0.000001787726,0.04990784,0.0002375171,0.0002551394,0.0001689537,0.00009752773,0.00005090454,0.00002387942,0.007768728,0.9414574],"study_design_scores_gemma":[0.0001330673,0.00003845475,0.00007570416,0.05752413,0.0001936767,0.00007481736,0.00002495703,0.00002361544,0.0007814675,0.00006795753,0.9400643,0.0009978156],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002332672,0.9931282,0.0001169378,0.00005379651,0.0001983602,0.001105954,0.001577175,0.001299135,0.0001877307],"genre_scores_gemma":[0.000271782,0.9949272,0.003700476,0.00007198277,0.0001480137,0.0001781992,0.0005117846,0.000146535,0.00004401178],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9404595,"threshold_uncertainty_score":0.9994835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04284914536003061,"score_gpt":0.3215402019546726,"score_spread":0.2786910565946421,"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."}}