{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008393699,0.002595845,0.002184876,0.002515848,0.001044203,0.003333054,0.004932438,0.002080787,0.01777774],"category_scores_gemma":[0.02973067,0.002210845,0.001860667,0.001456068,0.001713124,0.002352134,0.003283119,0.003781338,0.009294185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001912328,"about_ca_system_score_gemma":0.004458695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004437446,"about_ca_topic_score_gemma":0.004879051,"domain_scores_codex":[0.9955363,0.001120429,0.0003635465,0.0008592652,0.001884207,0.0002363029],"domain_scores_gemma":[0.989759,0.006065055,0.001047073,0.001060672,0.001688543,0.0003797141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00211282,0.0003941401,0.01882395,0.002240539,0.0009505269,0.0009045529,0.0009778934,0.2766934,0.07355969,0.07276309,0.1312448,0.4193346],"study_design_scores_gemma":[0.0001564668,0.00008655401,0.001079688,0.00008531034,0.0000520102,0.0001862006,0.00003857271,0.9110248,0.04306924,0.02803225,0.01602314,0.0001658238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004561812,0.0001338775,0.9101856,0.0001675659,0.00007261075,0.0001035825,0.002358444,0.08140967,0.001006844],"genre_scores_gemma":[0.08486164,0.0002232849,0.8864102,0.0004956112,0.0001089159,0.0007629733,0.007109825,0.01729614,0.00273136],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01777774,"threshold_uncertainty_score":0.05947244,"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."}}