{"id":"W4390100413","doi":"10.1145/3589132.3625622","title":"PathletRL: Trajectory Pathlet Dictionary Construction using Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Merge (version control); Trajectory; Reinforcement learning; Dictionary learning; Range (aeronautics); Artificial intelligence; Set (abstract data type); Speedup; Parallel computing","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":[0.00027718,0.0000998929,0.00008453849,0.0002316177,0.0002365622,0.0001551463,0.0003326721,0.00002935052,0.0000691359],"category_scores_gemma":[0.00001421314,0.00009534498,0.00004550979,0.0007120136,0.00003103661,0.0009212389,0.0003453251,0.0001056698,0.0002423253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004114656,"about_ca_system_score_gemma":0.00002317278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002805962,"about_ca_topic_score_gemma":7.420572e-7,"domain_scores_codex":[0.9989709,0.00003589808,0.0001775784,0.0002874547,0.000284442,0.0002436848],"domain_scores_gemma":[0.9995701,0.00002786336,0.00005918079,0.0002690908,0.00002792334,0.00004589073],"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":[0.000009073405,0.00005945504,0.006845975,0.00007446355,0.0001206244,0.0001986691,0.0008704048,0.1164328,0.003557328,0.1994761,0.01475399,0.6576011],"study_design_scores_gemma":[0.0002032189,0.00004590618,0.001503899,0.00001309822,0.000005267565,0.00001052802,0.0002253875,0.9687129,0.0002174556,0.0005922988,0.02830458,0.0001654657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01378068,0.00001296375,0.9721866,0.0001725336,0.001025023,0.0001372367,9.9824e-7,0.001153874,0.01153006],"genre_scores_gemma":[0.8031004,0.0001792125,0.1816944,0.0009663642,0.0006431525,0.00004744003,0.0002070834,0.00004411265,0.0131178],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8522801,"threshold_uncertainty_score":0.3888055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02792953563845239,"score_gpt":0.2459044409341389,"score_spread":0.2179749052956865,"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."}}