{"id":"W2910261954","doi":"10.48550/arxiv.1901.05044","title":"A linear programming approach to the tracking of partials","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Linear programming; Viterbi algorithm; Algorithm; Computer science; Time complexity; Tracking (education); Exponential function; Dynamic programming; Mathematical optimization; Mathematics; Decoding methods","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":[],"consensus_categories":[],"category_scores_codex":[0.0007578193,0.0001942708,0.0002882303,0.0001919099,0.00007612359,0.0001180573,0.002211055,0.0001899483,0.00000243746],"category_scores_gemma":[0.00004808886,0.0001722655,0.0001786295,0.000627354,0.00005314279,0.0002323784,0.001628086,0.0004257465,0.00003323051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005459847,"about_ca_system_score_gemma":0.0001601471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007215011,"about_ca_topic_score_gemma":0.000008819894,"domain_scores_codex":[0.9984919,0.0002239388,0.0002256523,0.0007041318,0.0001212802,0.0002331236],"domain_scores_gemma":[0.9979734,0.00008957408,0.0002902687,0.001392863,0.0001770895,0.00007675169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001636115,0.0001947665,0.0007455665,0.0001162584,0.00007594145,0.000008857806,0.004090856,0.6456043,0.00006367314,0.3441213,0.0003245737,0.004637627],"study_design_scores_gemma":[0.0002490092,0.0001175728,0.0004639619,0.0001488322,0.00005927326,0.000003661641,0.0002759626,0.976294,0.004024489,0.008696624,0.00913696,0.000529661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04455578,0.00002167582,0.9510847,0.0002259191,0.0001416928,0.0008209801,0.000003630857,0.00026652,0.002879148],"genre_scores_gemma":[0.9607432,0.00001385965,0.03853647,0.0001836905,0.00004319321,0.000004008454,0.000005008712,0.00001266001,0.0004578378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9161875,"threshold_uncertainty_score":0.7024784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1247655163127,"score_gpt":0.2248803048313508,"score_spread":0.1001147885186507,"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."}}