{"id":"W2563680521","doi":"","title":"Optimization of Highly Architectured Stereolithographic Microtrusses","year":2015,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Psychology; Computer science","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":[0.000275506,0.0007902375,0.000343436,0.0003535073,0.0002423661,0.0004679581,0.0005997516,0.0003828121,0.001634073],"category_scores_gemma":[0.0006186376,0.0004645754,0.0003024533,0.0003257065,0.0003405811,0.0005323518,0.0004561083,0.000464131,0.0004772948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006218401,"about_ca_system_score_gemma":0.0007081865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007859119,"about_ca_topic_score_gemma":0.004018558,"domain_scores_codex":[0.999728,0.0000166176,0.0000148474,0.00005728823,0.0001305613,0.00005248607],"domain_scores_gemma":[0.9996295,0.0001004287,0.0001111154,0.00008184001,0.00005535122,0.00002182751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008315036,0.00009741532,0.001003371,0.0002542798,0.00002008703,0.000132102,0.00006525202,0.1885755,0.7708201,0.002636489,0.0004006742,0.03591153],"study_design_scores_gemma":[0.00003398237,0.0007611205,0.003629667,0.00001966978,0.0000332276,0.0001509414,0.00008390529,0.293754,0.6953368,0.001039233,0.005117035,0.00004054231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7945129,0.0006834333,0.1922107,0.000115814,0.00008444649,0.0001709697,0.0004345973,0.001369576,0.01041764],"genre_scores_gemma":[0.8753784,0.0002970692,0.1214825,0.00001841454,0.00001087291,0.0001028772,0.0002157386,0.0001009938,0.002393198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001634073,"threshold_uncertainty_score":0.005466461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01028961393554602,"score_gpt":0.2153095107860685,"score_spread":0.2050198968505225,"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."}}