{"id":"W3109055445","doi":"10.1002/cyto.b.21970","title":"Best practices for optimization and validation of flow cytometry‐based receptor occupancy assays","year":2020,"lang":"en","type":"article","venue":"Cytometry Part B Clinical Cytometry","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Caprion (Canada)","funders":"American Association of Pharmaceutical Scientists","keywords":"Flow cytometry; Computational biology; Occupancy; Computer science; Drug development; Identification (biology); Receptor; Drug; Biology; Pharmacology; Immunology; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001320779,0.0003532792,0.0006722065,0.0002803857,0.000145269,0.00008559292,0.0003719912,0.0006324719,0.0001125411],"category_scores_gemma":[0.008492949,0.0003553964,0.0003530993,0.001242228,0.0002566287,0.0000408396,0.0001176834,0.0003133505,0.00001905229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002033384,"about_ca_system_score_gemma":0.0001796655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008331434,"about_ca_topic_score_gemma":0.00000170907,"domain_scores_codex":[0.9968188,0.0002804536,0.001183887,0.0009359336,0.0003831401,0.0003977794],"domain_scores_gemma":[0.9969134,0.0007688334,0.0009800772,0.0004742833,0.0004615027,0.0004018522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004632278,0.002237474,0.5094001,0.001325576,0.0006998548,0.000005596353,0.00008253414,0.005530797,0.4455431,0.00003290196,0.008858614,0.02165124],"study_design_scores_gemma":[0.01081549,0.009332918,0.00402915,0.0002187746,0.0007162217,0.0000100276,0.0002549901,0.06150297,0.8051535,0.00002476447,0.1062991,0.001642103],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5678057,0.0009822102,0.4278875,0.0007768825,0.0008464711,0.0007612456,0.00049017,0.00006210441,0.0003876953],"genre_scores_gemma":[0.9324888,0.0006843861,0.063191,0.001088668,0.001251291,0.00004477592,0.001041046,0.00007610607,0.0001339261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5053709,"threshold_uncertainty_score":0.9998898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1555814999466496,"score_gpt":0.385775363086806,"score_spread":0.2301938631401564,"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."}}