{"id":"W2087252391","doi":"10.1007/s10856-011-4309-4","title":"Direct visualization and quantification of bone growth into porous titanium implants using micro computed tomography","year":2011,"lang":"en","type":"article","venue":"Journal of Materials Science Materials in Medicine","topic":"Orthopaedic implants and arthroplasty","field":"Medicine","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Materials science; Biomedical engineering; Titanium; Porosity; X-ray microtomography; Tomography; Osseointegration; Bone tissue; Scanning electron microscope; Histology; Implant; Microscopy; Medicine; Radiology; Composite material; Pathology; Surgery; Metallurgy","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":[0.0004760812,0.0004092148,0.0002242521,0.0008728452,0.0001975433,0.0007054671,0.0004422035,0.0005850142,0.0009470527],"category_scores_gemma":[0.001111475,0.0005955268,0.0001918594,0.000433783,0.0007144889,0.0007492558,0.000631075,0.0005510915,0.0001755804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003783024,"about_ca_system_score_gemma":0.000642068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003212837,"about_ca_topic_score_gemma":0.003103622,"domain_scores_codex":[0.9997293,0.00003108989,0.00001271129,0.00003829063,0.0001578834,0.00003063055],"domain_scores_gemma":[0.9992853,0.0004146101,0.0001064338,0.0000484329,0.0001123306,0.00003296437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002918805,0.00002116407,0.001530509,0.0001074611,0.000008184506,0.00007983914,0.0000789452,0.001044658,0.9893273,0.0002919562,0.00007183348,0.007146293],"study_design_scores_gemma":[0.00008283954,0.0004596606,0.02295299,0.00003246993,0.00007819242,0.001185431,0.0001570538,0.02014715,0.9527848,0.0003334055,0.001734552,0.00005158833],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8825308,0.006234796,0.105789,0.0002445506,0.00004844662,0.0001836432,0.0004754332,0.0004323794,0.004060969],"genre_scores_gemma":[0.9268797,0.001884236,0.06898315,0.00007069339,0.00003871695,0.00008989692,0.0001745367,0.00007213961,0.001806943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003212837,"threshold_uncertainty_score":0.006388307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03796021620437064,"score_gpt":0.309681180443789,"score_spread":0.2717209642394183,"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."}}