{"id":"W3138612847","doi":"10.1101/2021.03.11.434956","title":"Pheniqs 2.0: accurate, high performance Bayesian decoding and confidence estimation for combinatorial barcode indexing","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"QR Code Applications and Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; New York University Abu Dhabi","keywords":"Barcode; Search engine indexing; Decoding methods; Computer science; Bayesian probability; Information retrieval; Data mining; Artificial intelligence; Algorithm; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004775351,0.0004387876,0.0004794932,0.0002098606,0.0004406334,0.001122463,0.001306845,0.0004853084,0.000003633438],"category_scores_gemma":[0.0002864886,0.0004955007,0.00007501114,0.000583523,0.0001285179,0.0008025088,0.001311217,0.0005450203,0.000004043116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001729106,"about_ca_system_score_gemma":0.0004985217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003448315,"about_ca_topic_score_gemma":0.000001438875,"domain_scores_codex":[0.9974452,0.0000522407,0.0005141069,0.001193928,0.0003020514,0.000492503],"domain_scores_gemma":[0.9971685,0.0001924921,0.0004203075,0.001527588,0.0005528633,0.0001381942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000363535,0.0002812791,0.005954665,0.001931985,0.000270298,0.00003830885,0.0001076076,0.002711225,0.2894399,0.697993,0.0001979452,0.001037424],"study_design_scores_gemma":[0.0009102926,0.00009277409,0.02581715,0.0007144146,0.00006938221,1.243005e-7,0.000009248456,0.5948204,0.3752097,0.0007391701,0.0004056375,0.001211639],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4131607,0.0003587777,0.5842991,0.0002848811,0.0007217214,0.0005755022,0.00002051841,0.0005761096,0.000002696617],"genre_scores_gemma":[0.7713293,0.0002630118,0.2276651,0.00005585567,0.0001062827,0.0005414083,4.833241e-7,0.00003782155,7.395626e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6972539,"threshold_uncertainty_score":0.9999145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01503582343859952,"score_gpt":0.2310125917647367,"score_spread":0.2159767683261372,"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."}}