{"id":"W2965191442","doi":"10.11159/iccpe19.01","title":"Ultrasensitive Detection of Water Contaminants, Biomarkers and illegal Drugs Using Active 3D Metallic Nanostructures","year":2019,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Contamination; Nanostructure; Materials science; Nanotechnology; Environmental chemistry; Metal; Chemistry; Metallurgy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001057968,0.0002929332,0.0001434927,0.0001895702,0.0001191513,0.0004057662,0.000329083,0.000549558,0.0003654726],"category_scores_gemma":[0.0001637663,0.0002572271,0.000273825,0.00009664057,0.0002995481,0.000250038,0.0003287488,0.0002850879,0.0003301109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003151589,"about_ca_system_score_gemma":0.000141521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005022295,"about_ca_topic_score_gemma":0.0008495816,"domain_scores_codex":[0.9998845,0.00001030321,0.000005906154,0.00003361182,0.00004777408,0.00001782798],"domain_scores_gemma":[0.9999062,0.00002495528,0.00002572098,0.0000154981,0.0000197735,0.000007765483],"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":[0.000006860756,0.000005895454,0.0000983139,0.00002382241,0.000002513686,0.00003489433,0.00001462636,0.0002380721,0.9970399,0.0001380558,0.00004624168,0.002350864],"study_design_scores_gemma":[0.000004679938,0.00003644366,0.0005461109,0.000002991477,0.000005966035,0.0001244908,0.00001314977,0.006606783,0.9909647,0.00009023324,0.001596343,0.000008123253],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9082469,0.001968446,0.08197938,0.0003176608,0.0001192716,0.00008414694,0.0003593787,0.001015421,0.005909353],"genre_scores_gemma":[0.9111375,0.0009162116,0.08417684,0.0001983222,0.00002266162,0.00007055962,0.0002282295,0.00004765578,0.003201927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000549558,"threshold_uncertainty_score":0.002286673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00314906570664579,"score_gpt":0.2018363820439515,"score_spread":0.1986873163373057,"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."}}