{"id":"W2011530951","doi":"10.1002/pamm.201110040","title":"Computational Simulation of Bone Remodeling using Design Space Topology Optimization","year":2011,"lang":"en","type":"article","venue":"PAMM","topic":"Orthopaedic implants and arthroplasty","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trabecular bone; Computer science; Bone remodeling; Topology optimization; Anisotropy; Enhanced Data Rates for GSM Evolution; Simulation; Biomedical engineering; Topology (electrical circuits); Structural engineering; Physics; Mathematics; Finite element method; Engineering; Artificial intelligence; Osteoporosis; Biology; Optics","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":[0.0001244588,0.00005173943,0.0001240114,0.00006444365,0.00003949737,0.000001827814,0.0000110698,0.00004205381,0.000211666],"category_scores_gemma":[0.00005373171,0.0000464976,0.00002550304,0.00006035058,0.0000368334,0.00004899688,0.000009757695,0.00004091973,0.000003544242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001269089,"about_ca_system_score_gemma":0.00004596611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002324888,"about_ca_topic_score_gemma":4.542831e-7,"domain_scores_codex":[0.9995221,0.00002816686,0.0001740209,0.00008995528,0.00009857937,0.00008716948],"domain_scores_gemma":[0.9996778,0.00005278084,0.00008337765,0.00006213445,0.00008928831,0.00003460488],"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":[0.0001057511,0.00004212869,0.002125148,0.00001428296,0.00001016901,0.000006082754,0.0003022525,0.9939389,0.0003022311,0.0001964655,0.00001006622,0.002946542],"study_design_scores_gemma":[0.0005158935,0.00009212067,0.0009125625,0.00003171712,0.00003254266,0.00005465969,0.0000474216,0.9976724,0.0003424668,0.0002140388,0.00004045565,0.00004370189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1307534,0.00004625901,0.8684387,0.00003313665,0.0001087417,0.0001075001,0.00000182832,0.0000173496,0.0004930597],"genre_scores_gemma":[0.7392414,0.000004692991,0.2606167,0.00004296145,0.00004199508,3.788793e-7,0.00000999303,0.000006187946,0.00003561943],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.608488,"threshold_uncertainty_score":0.2317595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09702911375410873,"score_gpt":0.308906030367017,"score_spread":0.2118769166129083,"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."}}