{"id":"W1600246822","doi":"10.1002/jmr.2358","title":"On-line kinetic model discrimination for optimized surface plasmon resonance experiments","year":2014,"lang":"en","type":"article","venue":"Journal of Molecular Recognition","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Surface plasmon resonance; Kinetic energy; Resonance (particle physics); Throughput; Line (geometry); Surface plasmon; Biological system; Kinetics; Plasmon; Simple (philosophy); Chemistry; Computer science; Physics; Materials science; Nanotechnology; Optics; Mathematics; Atomic physics; Nanoparticle; Classical mechanics; Telecommunications; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001633355,0.001583325,0.001173609,0.0006644334,0.0003664077,0.001198482,0.001460787,0.001103806,0.002569742],"category_scores_gemma":[0.005244055,0.0007544024,0.0009329368,0.0005498936,0.0003296206,0.001061276,0.001114507,0.001536394,0.00135991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007239249,"about_ca_system_score_gemma":0.0008999968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001197458,"about_ca_topic_score_gemma":0.001512616,"domain_scores_codex":[0.9986084,0.0003747273,0.00007351318,0.0002657873,0.0005526439,0.0001249456],"domain_scores_gemma":[0.997361,0.001374991,0.0003030286,0.0004198017,0.0004809797,0.00006018358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007437615,0.001057311,0.002440653,0.0004308065,0.0001216472,0.0001851457,0.000270243,0.2611146,0.4799209,0.004826877,0.002297467,0.2465907],"study_design_scores_gemma":[0.00001575598,0.0001129547,0.0003977966,0.00000486405,0.00001119371,0.00004974094,0.00001118707,0.900615,0.09715039,0.000717906,0.0008881931,0.00002509853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02939326,0.00004856863,0.9677238,0.00006140845,0.00001246662,0.0001207739,0.00007984493,0.00164151,0.0009184895],"genre_scores_gemma":[0.2552729,0.0001165209,0.7414533,0.0001074667,0.00001290554,0.0004304574,0.0003387171,0.0006946534,0.00157318],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002569742,"threshold_uncertainty_score":0.008638144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02812889164230777,"score_gpt":0.3088619536242559,"score_spread":0.2807330619819482,"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."}}