{"id":"W4200633797","doi":"","title":"Plasmonic biochip for “SPRI, SERS and bio-imaging” in vitro monitoring of bio, molecular to cellular, surface interactions","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Biochip; Nanotechnology; Plasmon; In vitro; Materials science; Computer science; Chemistry; Cell biology; Biology; Optoelectronics; Biochemistry","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":[0.0006203641,0.001048292,0.0006631906,0.0004302809,0.0005014072,0.0007863123,0.0006600986,0.001027389,0.004058495],"category_scores_gemma":[0.000544094,0.0005500522,0.0003994809,0.000338309,0.0005610746,0.0006061326,0.0004386927,0.000970227,0.00315442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006133883,"about_ca_system_score_gemma":0.0003744608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005331486,"about_ca_topic_score_gemma":0.0008248267,"domain_scores_codex":[0.9996185,0.00006814569,0.00001615974,0.0001182189,0.0001270461,0.00005182511],"domain_scores_gemma":[0.9997471,0.0001090643,0.00002306404,0.00004006066,0.00005629141,0.00002440885],"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":[0.00003634138,0.00001549297,0.00004696111,0.00009527722,0.000006947909,0.00004132298,0.00001437474,0.000277154,0.9945747,0.0008920666,0.0003422874,0.003657008],"study_design_scores_gemma":[0.000003755818,0.00003265659,0.0002825199,0.000004939656,0.00001026306,0.00009591763,0.000008429421,0.00254764,0.9932927,0.0003203627,0.003395797,0.00000494138],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4422535,0.01947584,0.4801324,0.003675195,0.001933235,0.0003607979,0.001800231,0.002529432,0.0478394],"genre_scores_gemma":[0.8189052,0.007353805,0.1274953,0.000843534,0.0002642726,0.0002695294,0.001659509,0.0004969178,0.04271201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004058495,"threshold_uncertainty_score":0.01357704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01025273876212228,"score_gpt":0.2593814654860119,"score_spread":0.2491287267238896,"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."}}