{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009556587,0.001953213,0.002710536,0.000866007,0.0006391174,0.002002725,0.002441218,0.001153233,0.0008761863],"category_scores_gemma":[0.01927264,0.001075768,0.0009392434,0.0009094073,0.001117696,0.001874071,0.001422692,0.001848236,0.0005103737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001429967,"about_ca_system_score_gemma":0.001551638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001083323,"about_ca_topic_score_gemma":0.001337948,"domain_scores_codex":[0.9940171,0.001880396,0.0004848105,0.001075531,0.00220473,0.0003374091],"domain_scores_gemma":[0.9770592,0.01645445,0.001834776,0.002248732,0.002173182,0.0002296353],"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":[0.001187839,0.0009566605,0.003039212,0.00036497,0.0001278087,0.0001771013,0.0002482246,0.1842214,0.7185391,0.002397554,0.0003430113,0.08839718],"study_design_scores_gemma":[0.000047267,0.0003989746,0.001423927,0.00001337799,0.00003397467,0.00008150277,0.00003036913,0.5512231,0.4448802,0.001316516,0.0004835191,0.00006721617],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1589922,0.0002591011,0.8382003,0.0001418779,0.00001956306,0.000230435,0.0001007883,0.0013932,0.0006624505],"genre_scores_gemma":[0.484739,0.0002280901,0.5134592,0.0000620482,0.00001708244,0.000609546,0.0002022276,0.0001856315,0.0004972524],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009556587,"threshold_uncertainty_score":0.05054069,"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."}}