{"id":"W2060190764","doi":"10.1002/jmr.894","title":"Online optimization of surface plasmon resonance‐based biosensor experiments for improved throughput and confidence","year":2008,"lang":"en","type":"article","venue":"Journal of Molecular Recognition","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Surface plasmon resonance; Biosensor; Macromolecule; Biological system; Identification (biology); Throughput; Computer science; Resonance (particle physics); Chemistry; Nanotechnology; Materials science; Physics; Nanoparticle; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001643406,0.00009775531,0.0002834881,0.00009226613,0.00005181529,0.000005775237,0.00004424217,0.00007035201,0.00002500931],"category_scores_gemma":[0.0001803687,0.0000798011,0.0001184996,0.00009510537,0.0001046926,0.00008191782,0.0000126592,0.0001260462,6.305206e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002520921,"about_ca_system_score_gemma":0.0001235949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001881472,"about_ca_topic_score_gemma":0.000001026249,"domain_scores_codex":[0.9990541,0.00004628207,0.0003911914,0.000116095,0.0002618052,0.0001304869],"domain_scores_gemma":[0.9988033,0.00009678815,0.0002769349,0.00006938416,0.000657447,0.00009615129],"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":[0.004361839,0.0006036474,0.0005506429,0.0002079151,0.000108898,0.0001262939,0.0001203025,0.0007371097,0.9831108,0.00001339099,0.0002420719,0.00981703],"study_design_scores_gemma":[0.004161136,0.002193147,0.001518401,0.0004416713,0.00008203064,0.0005302499,0.00008054571,0.02364233,0.9665335,0.0001142508,0.0005878418,0.0001148821],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785546,0.00175928,0.01833738,0.0008355301,0.00006266521,0.000312439,0.00006750118,0.000004486785,0.00006611474],"genre_scores_gemma":[0.8952841,0.001763006,0.1023256,0.0003379828,0.00007717504,0.000002986938,0.00009603004,0.00001881415,0.00009432342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08398827,"threshold_uncertainty_score":0.3254194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07597250086295905,"score_gpt":0.3429502244302604,"score_spread":0.2669777235673014,"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."}}