{"id":"W2994918970","doi":"","title":"Next generation of biochips for SPRI, SERS and bio-imaging: taking advantage of hydrid plasmonics modes","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; Plasmon; Computer science; Nanotechnology; Optoelectronics; Physics; Materials 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":[0.0008594135,0.0008500269,0.0009177558,0.0003531447,0.0003110366,0.001505572,0.0009292247,0.001332949,0.006205281],"category_scores_gemma":[0.0004210514,0.000586993,0.0005481346,0.0002639538,0.0005075209,0.001632876,0.001206794,0.001502671,0.003264824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006404372,"about_ca_system_score_gemma":0.0003992763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00031356,"about_ca_topic_score_gemma":0.0004431236,"domain_scores_codex":[0.9996907,0.00003286923,0.0000108956,0.00007889717,0.0001250579,0.00006171119],"domain_scores_gemma":[0.9996502,0.0000916158,0.00002868301,0.0000593932,0.0001008532,0.00006918763],"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":[0.00009361834,0.0000573696,0.0002268283,0.0002463151,0.00002106478,0.00005430081,0.00005000297,0.0004627479,0.9627735,0.005542431,0.00223994,0.02823175],"study_design_scores_gemma":[0.00002715024,0.0002536517,0.0007464049,0.00002007497,0.0000347382,0.0002358603,0.0000434298,0.007727012,0.9419351,0.00273236,0.04621039,0.00003389032],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4119779,0.04456869,0.4634408,0.009918516,0.003343741,0.0005381672,0.002878072,0.004801627,0.05853239],"genre_scores_gemma":[0.4915827,0.01244342,0.4493274,0.001884498,0.000603074,0.0003496929,0.002338529,0.000631774,0.04083896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006205281,"threshold_uncertainty_score":0.02075875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02808555442510017,"score_gpt":0.268063512765724,"score_spread":0.2399779583406239,"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."}}