{"id":"W4280577380","doi":"10.3390/mi13050773","title":"Interlacing Infills for Multi-Material Fused Filament Fabrication Using Layered Depth Material Images","year":2022,"lang":"en","type":"article","venue":"Micromachines","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interlacing; Slicing; Interlocking; Interface (matter); Material properties; Joint (building); Computer science; Tracing; Material efficiency; Infill; Process (computing); Materials science; Engineering drawing; Structural engineering; Composite material; Engineering; Artificial intelligence; Computer graphics (images)","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":[],"consensus_categories":[],"category_scores_codex":[0.0001365948,0.0001937646,0.0001943887,0.0001349128,0.0002844048,0.00008634764,0.0002384907,0.00004686814,0.0001420376],"category_scores_gemma":[0.0000462947,0.0001968796,0.00006693787,0.00007009721,0.00003070297,0.00007748939,0.0002725488,0.0001190038,0.000003479078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001326825,"about_ca_system_score_gemma":0.000007449818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008521767,"about_ca_topic_score_gemma":0.00000664308,"domain_scores_codex":[0.9991521,0.0000337583,0.0002366163,0.0002156552,0.00007857905,0.000283348],"domain_scores_gemma":[0.9996505,0.00003507077,0.0000693949,0.0002003093,0.00002395039,0.00002072634],"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":[0.00003982185,0.0000284472,0.0003291153,0.0000833973,0.00004645754,0.00000248033,0.0002331892,0.008673396,0.9801624,0.00001035539,0.0009744914,0.009416426],"study_design_scores_gemma":[0.0004669047,0.00004764477,0.00290825,0.00001982733,0.00001806791,0.00001422706,0.0001792093,0.02398103,0.9692138,0.0001089458,0.002772739,0.0002693875],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9493874,0.00003375489,0.04733615,0.00002613165,0.001265757,0.0002971934,0.0006173473,0.001001978,0.00003429872],"genre_scores_gemma":[0.9632752,0.000003402121,0.036128,0.00001642871,0.0001486672,0.0001207347,0.0002064167,0.00005128307,0.00004988304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01530764,"threshold_uncertainty_score":0.8028515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02881996669029397,"score_gpt":0.2634519441724628,"score_spread":0.2346319774821688,"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."}}