{"id":"W3172747666","doi":"10.3934/mbe.2021224","title":"Numerical assessment of directional energy performance for 3D printed midsole structures","year":2021,"lang":"en","type":"article","venue":"Mathematical Biosciences & Engineering","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Energy (signal processing); Measure (data warehouse); Computer science; Energy transfer; Topology (electrical circuits); Efficient energy use; Energy transformation; Mechanical engineering; Simulation; Engineering; Physics; Electrical engineering; Engineering physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001091092,0.0001428015,0.0002176025,0.00008413824,0.000053354,0.00002882565,0.0001874933,0.00006529668,0.00005989741],"category_scores_gemma":[0.0001743347,0.000123226,0.00006711599,0.0002292904,0.00006984699,0.00006932489,0.00006121127,0.0001069924,0.000001156482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003838963,"about_ca_system_score_gemma":0.00002211484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.712344e-7,"about_ca_topic_score_gemma":1.82401e-7,"domain_scores_codex":[0.9991227,0.000005313455,0.0002362915,0.0001852951,0.0001959459,0.000254388],"domain_scores_gemma":[0.999519,0.0001904024,0.00002928865,0.0001677373,0.00004963822,0.00004396616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006598154,0.0001936163,0.0007727243,0.002146872,0.0002074191,0.000009408872,0.0002119694,0.4730739,0.1900728,0.2704676,0.0003026276,0.06253447],"study_design_scores_gemma":[0.00006148928,0.00002534507,0.002136173,0.00006027579,0.000006742892,0.00001027713,0.00002244012,0.5690035,0.4254711,0.001479251,0.00159181,0.0001315481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1937805,0.00005902714,0.8041343,0.0000358729,0.0002469685,0.00005653946,0.000009265422,0.0004847581,0.001192783],"genre_scores_gemma":[0.7863913,0.00001330081,0.2134824,0.000004398357,0.00002875482,0.00002757712,0.00000343556,0.00001257901,0.00003618515],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5926109,"threshold_uncertainty_score":0.5025008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01152895536830754,"score_gpt":0.2365831131822556,"score_spread":0.2250541578139481,"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."}}