{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004166903,0.0003739421,0.0003386808,0.0006622627,0.000348719,0.0009872608,0.0005001299,0.001341932,0.003232558],"category_scores_gemma":[0.001501523,0.0001940799,0.0004892267,0.000523373,0.0007241322,0.0004258312,0.0004239173,0.0003919564,0.0002851311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004227305,"about_ca_system_score_gemma":0.0002918905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001549044,"about_ca_topic_score_gemma":0.001472202,"domain_scores_codex":[0.9998374,0.00003036922,0.00001051822,0.00002314457,0.00007190399,0.00002671446],"domain_scores_gemma":[0.9992205,0.0005135211,0.0000796196,0.00006354884,0.00009292304,0.00002990377],"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":[0.0001110178,0.00007930327,0.002290774,0.0001303942,0.00002176643,0.0001937702,0.000123355,0.9701322,0.01279013,0.004952302,0.0004377478,0.008737215],"study_design_scores_gemma":[0.000007869973,0.00004362489,0.000555605,0.00001132661,0.000005079333,0.00004106837,0.00004146542,0.9953398,0.002841723,0.0007124635,0.0003903979,0.000009692584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8562259,0.0006456684,0.1049744,0.0006149054,0.0001090187,0.0000720268,0.0005912579,0.0004606404,0.03630618],"genre_scores_gemma":[0.980184,0.0001168757,0.0177247,0.00002999979,0.00000481883,0.00003592844,0.0001295381,0.00002648742,0.001747737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003232558,"threshold_uncertainty_score":0.01081401,"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."}}