{"id":"W3147999427","doi":"10.1142/s2047684120500232","title":"Refining anticipation of degraded bone microstructures during osteoporosis based on statistical homogenized reconstruction method via quality of connection function","year":2020,"lang":"en","type":"article","venue":"International Journal of Computational Materials Science and Engineering","topic":"Composite Material Mechanics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Ministry of Science Research and Technology","keywords":"Computer science; Osteoporosis; Interpolation (computer graphics); Volume fraction; Function (biology); Statistical model; Algorithm; Quality (philosophy); Mathematical optimization; Mathematics; Materials science; Artificial intelligence; Motion (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":[0.0007215738,0.0004688321,0.0004234371,0.0008947214,0.0002243199,0.0006722055,0.0006659013,0.0007427222,0.001183835],"category_scores_gemma":[0.002333863,0.0003232004,0.0005233695,0.0004495579,0.0004659205,0.0008119696,0.0004777405,0.0004404845,0.0001735081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004996736,"about_ca_system_score_gemma":0.0009325831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004614842,"about_ca_topic_score_gemma":0.005012134,"domain_scores_codex":[0.9998283,0.00004631585,0.00001026463,0.00003322055,0.00006104876,0.00002081452],"domain_scores_gemma":[0.9993601,0.0002794293,0.000110996,0.00005970952,0.0001505641,0.00003914707],"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":[0.0001676839,0.00006000544,0.007756877,0.0001695657,0.00005317987,0.00031858,0.0001763345,0.8917046,0.02768334,0.01054806,0.0008722987,0.06048963],"study_design_scores_gemma":[0.000002203804,0.0000112262,0.0004055443,0.000002846183,0.000004659927,0.0000241555,0.000007344845,0.9976876,0.001039074,0.0006679756,0.0001421439,0.00000527006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1133488,0.0002055185,0.8847939,0.0001613495,0.00001750052,0.00002907923,0.00008397952,0.0003322411,0.001027619],"genre_scores_gemma":[0.8186897,0.0003213152,0.1789413,0.00005537856,0.00001621943,0.00006256199,0.000258175,0.0001888996,0.001466428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004614842,"threshold_uncertainty_score":0.009175956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01585562241535795,"score_gpt":0.2671650960838776,"score_spread":0.2513094736685197,"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."}}