{"id":"W3215970349","doi":"10.1021/acsmeasuresciau.1c00037","title":"Efficient Simulation of Arbitrary Multicomponent First-Order Binding Kinetics for Improved Assay Design and Molecular Assembly","year":2021,"lang":"en","type":"article","venue":"ACS Measurement Science Au","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada","keywords":"Sensitivity (control systems); Workflow; Computer science; Receptor–ligand kinetics; Biological system; Immunoassay; Kinetics; Physics; Electronic engineering; Engineering; 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":[0.0009410079,0.0001035947,0.00009840864,0.00004381837,0.00016787,0.00002335223,0.000124143,0.00005467184,3.111912e-7],"category_scores_gemma":[0.0005879297,0.00009954518,0.00003377498,0.0002233387,0.0001532077,0.000004060862,0.00009126964,0.00003454261,1.978376e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007053584,"about_ca_system_score_gemma":0.0002037459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003285418,"about_ca_topic_score_gemma":0.000004314836,"domain_scores_codex":[0.9989028,0.00002655209,0.0001864986,0.0003639209,0.0003061306,0.0002140948],"domain_scores_gemma":[0.9987211,0.00002987081,0.0001050227,0.0002820951,0.000800614,0.00006134483],"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":[0.00001329239,0.00008918099,0.00002351017,0.0000124888,0.000007575653,2.373383e-7,0.00001533333,0.04529787,0.9533577,0.00008217055,0.000008820368,0.001091854],"study_design_scores_gemma":[0.0002240998,0.0001061178,0.0001566233,0.00002171313,0.00001560037,0.000001663078,0.0000172798,0.1590687,0.8396459,0.00009354094,0.0005441827,0.000104582],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3428396,0.00007891152,0.6564627,0.0002255188,0.00003123812,0.0003278646,0.000003777508,0.000008937342,0.00002142659],"genre_scores_gemma":[0.8456776,0.00001100962,0.1541704,0.00007797409,0.00001638152,0.00002274578,0.000009878233,0.000009062644,0.00000492703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.502838,"threshold_uncertainty_score":0.4059335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03848675608404142,"score_gpt":0.3002984704855547,"score_spread":0.2618117144015134,"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."}}