{"id":"W2474376112","doi":"10.1021/acsami.6b03644","title":"Effect of Extreme Wettability on Platelet Adhesion on Metallic Implants: From Superhydrophilicity to Superhydrophobicity","year":2016,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Polymer Surface Interaction Studies","field":"Materials Science","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Michael Smith Health Research BC","keywords":"Wetting; Superhydrophilicity; Materials science; Contact angle; Adhesion; Biocompatibility; Surface roughness; Protein adsorption; X-ray photoelectron spectroscopy; Titanium; Surface modification; Laser ablation; Adsorption; Surface finish; Nanotechnology; Chemical engineering; Thrombogenicity; Composite material; Platelet; Metallurgy; Polymer; Laser; Organic chemistry; Chemistry","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001219959,0.0006662311,0.001289651,0.0001774054,0.0001674968,0.0001165443,0.000758648,0.0002051205,0.003821169],"category_scores_gemma":[0.000307869,0.0004073679,0.00006716944,0.000147,0.00024619,0.0002615321,0.000484311,0.000128228,0.003780763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002145922,"about_ca_system_score_gemma":0.00002376387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008958118,"about_ca_topic_score_gemma":0.00004394594,"domain_scores_codex":[0.9959561,0.0005116184,0.001019621,0.001168132,0.0006846973,0.0006597955],"domain_scores_gemma":[0.9966874,0.001673804,0.0003421703,0.00105445,0.00008117235,0.000161033],"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":[0.005032079,0.0001866261,0.00007484864,0.00004967377,0.00007757633,0.000005438578,0.0003411756,0.0000102293,0.9917144,0.00008980797,0.0004093383,0.002008771],"study_design_scores_gemma":[0.0009876055,0.001441724,0.0005151982,0.0002685485,0.00005072468,0.000003816432,0.0000427579,1.443279e-7,0.9957962,0.0002589911,0.0001966116,0.0004376564],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946277,0.00004252958,0.00001118694,0.0003586802,0.001677232,0.0009225243,0.001600069,0.0002353225,0.0005247522],"genre_scores_gemma":[0.9991259,0.00003671486,0.00006064703,0.0002512977,0.0001611636,0.000206783,0.00001337095,0.00006095528,0.0000831889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004498175,"threshold_uncertainty_score":0.9998378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02024003156230642,"score_gpt":0.2689936643358559,"score_spread":0.2487536327735494,"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."}}