{"id":"W2795664372","doi":"10.1007/s13534-018-0063-6","title":"Surface morphology characterization of laser-induced titanium implants: lesson to enhance osseointegration process","year":2018,"lang":"en","type":"article","venue":"Biomedical Engineering Letters","topic":"Laser Applications in Dentistry and Medicine","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Osseointegration; Kurtosis; Titanium; Materials science; Laser; Surface (topology); Characterization (materials science); Texture (cosmology); Morphology (biology); Root mean square; Implant; Composite material; Mathematics; Optics; Geometry; Nanotechnology; Medicine; Image (mathematics); Physics; Computer science","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":[0.0001699788,0.0001332622,0.0002444914,0.0001194289,0.00002970952,0.000005829362,0.0001185263,0.0001097778,0.0000829346],"category_scores_gemma":[0.0001602337,0.0001167507,0.00003330979,0.0004594271,0.00008612067,0.00005922403,0.00002178773,0.0001427858,0.00006240836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004673576,"about_ca_system_score_gemma":0.00004319318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001172145,"about_ca_topic_score_gemma":8.028042e-7,"domain_scores_codex":[0.9989218,0.00001041255,0.0003071503,0.0002546879,0.0002835691,0.0002223881],"domain_scores_gemma":[0.9993362,0.00003032491,0.00007146098,0.0002561328,0.0001146258,0.0001912351],"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":[0.00006101086,0.00007888057,0.000133962,0.0001358248,0.00002505435,0.00001681307,0.0001882667,0.000008328558,0.9970488,0.00001755197,0.0007067726,0.001578767],"study_design_scores_gemma":[0.0004173125,0.0003565147,0.009516386,0.000243405,0.00004374567,0.0001174222,0.00002162608,0.001304047,0.9821256,9.795223e-7,0.00572591,0.0001270227],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9190049,0.000002226073,0.07511365,0.004913831,0.00053692,0.0002823293,0.00002677743,0.00008857107,0.00003080453],"genre_scores_gemma":[0.9959384,0.000004400452,0.001864562,0.001233599,0.0005727024,0.0000267162,0.0002453902,0.00002062704,0.00009359728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07693352,"threshold_uncertainty_score":0.4760955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0099218587681763,"score_gpt":0.2962819484746558,"score_spread":0.2863600897064795,"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."}}