{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005211281,0.001068425,0.001052319,0.0007211568,0.0003986066,0.0006057803,0.001704545,0.001003728,0.004061592],"category_scores_gemma":[0.002280326,0.0005337407,0.0007438351,0.00089221,0.0005503718,0.001570263,0.001474185,0.002195793,0.001824629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006535909,"about_ca_system_score_gemma":0.001314669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00754154,"about_ca_topic_score_gemma":0.008272262,"domain_scores_codex":[0.9996437,0.00005211726,0.00002176693,0.0001287078,0.0001044746,0.00004919434],"domain_scores_gemma":[0.9992272,0.0002430528,0.00008654618,0.0001708364,0.0002047596,0.00006761263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002558625,0.0001712784,0.001940619,0.0002019654,0.0000807487,0.0002679142,0.0001318292,0.4234309,0.01218178,0.009140451,0.01929501,0.5329016],"study_design_scores_gemma":[0.00001552281,0.00003447668,0.0001231145,0.000007217654,0.000005173926,0.00005611541,0.00001132516,0.9935694,0.002334594,0.002072031,0.001762283,0.000008717152],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008772545,0.000154216,0.9871313,0.00009840068,0.00006631824,0.00007532477,0.0002880184,0.002450533,0.0009633823],"genre_scores_gemma":[0.2592259,0.0003661555,0.7283894,0.0003381494,0.00007701979,0.000332206,0.004074704,0.0006703102,0.006526154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00754154,"threshold_uncertainty_score":0.01499528,"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."}}