{"id":"W4212923685","doi":"10.2139/ssrn.4038110","title":"A Process Convolution Model for Crash Count Data on a Network","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; HEC Montréal","funders":"","keywords":"Count data; Crash; Computer science; Process (computing); Statistics; Convolution (computer science); Econometrics; Mathematics; Artificial intelligence; Programming language; Artificial neural network","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":[0.004950778,0.00107902,0.001685464,0.001837733,0.0008205192,0.001959943,0.003517011,0.003084577,0.003065906],"category_scores_gemma":[0.01301412,0.001020403,0.001628374,0.002632569,0.001285346,0.003932113,0.001719347,0.002590159,0.001168555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002242879,"about_ca_system_score_gemma":0.001802064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02197016,"about_ca_topic_score_gemma":0.0150879,"domain_scores_codex":[0.998494,0.0003878417,0.00009364653,0.000446068,0.0002901533,0.0002881321],"domain_scores_gemma":[0.9931628,0.004082981,0.0005799492,0.0008541779,0.001034491,0.0002856474],"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":[0.0002914116,0.0001679193,0.007980663,0.0001152525,0.0001178504,0.0002486472,0.000227907,0.8634437,0.0027866,0.08037441,0.003313929,0.04093179],"study_design_scores_gemma":[0.000003562985,0.00001327502,0.0003581138,0.000004725435,0.00001099725,0.0000255034,0.000006469908,0.9928772,0.000179159,0.00629751,0.0002156615,0.000007765193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08071691,0.0005079246,0.914115,0.0010374,0.0001370159,0.00008867867,0.0009231431,0.000877072,0.001596947],"genre_scores_gemma":[0.908636,0.001029552,0.07368839,0.0003171756,0.0003545065,0.0003286469,0.001889382,0.000198423,0.01355782],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02197016,"threshold_uncertainty_score":0.04368454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04158118083929049,"score_gpt":0.3369409990319969,"score_spread":0.2953598181927065,"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."}}