{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002723226,0.0002570025,0.0002264618,0.0002776929,0.0002111474,0.000471438,0.0003276377,0.0006998288,0.001519389],"category_scores_gemma":[0.0005346157,0.0002200025,0.0003259115,0.000246393,0.0002812798,0.0006584254,0.0001955732,0.0005345716,0.0005068417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000230217,"about_ca_system_score_gemma":0.0001679342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003597654,"about_ca_topic_score_gemma":0.0007567223,"domain_scores_codex":[0.9997438,0.00002955253,0.00001458307,0.00004492184,0.0001301033,0.00003708365],"domain_scores_gemma":[0.9997076,0.00005370686,0.00004716783,0.00003713621,0.0001359228,0.00001828506],"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":[0.00004018112,0.00001837777,0.0005576234,0.00005832252,0.000004569519,0.00004342734,0.00005783306,0.00008744965,0.9913089,0.0001146752,0.0001496806,0.007558825],"study_design_scores_gemma":[0.00001097173,0.0002887418,0.009809708,0.00001064786,0.00002661461,0.0003249186,0.0001679567,0.002602128,0.9824778,0.0002562356,0.004005541,0.00001865979],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9599598,0.005162393,0.0276614,0.001235201,0.0002738598,0.00006625139,0.0001525334,0.0002347701,0.005253743],"genre_scores_gemma":[0.982464,0.001389926,0.0120346,0.0002106841,0.00006040115,0.0000244253,0.0001079573,0.00008501575,0.003622938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001519389,"threshold_uncertainty_score":0.005082846,"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."}}