{"id":"W3115018051","doi":"10.3390/risks9010007","title":"Mining Actuarial Risk Predictors in Accident Descriptions Using Recurrent Neural Networks","year":2020,"lang":"en","type":"article","venue":"Risks","topic":"Topic Modeling","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Accident (philosophy); Computer science; Artificial neural network; Poisson regression; Regression; Artificial intelligence; Machine learning; Task (project management); Representation (politics); Profit (economics); Data mining; Econometrics; Statistics; Mathematics; Engineering","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":[0.0002070669,0.0001272568,0.0001562344,0.00006707521,0.0001151411,0.0001508402,0.0005410641,0.00007395785,0.00001044092],"category_scores_gemma":[0.0001297208,0.000128553,0.00006284655,0.0003352747,0.0000152936,0.0003852216,0.0003181339,0.0003462555,0.000004107626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008169627,"about_ca_system_score_gemma":0.0000442271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003476195,"about_ca_topic_score_gemma":0.00008316592,"domain_scores_codex":[0.998654,0.0001296187,0.0003062721,0.0003968236,0.0002014667,0.0003118357],"domain_scores_gemma":[0.9993807,0.00006548747,0.0001148685,0.0002692414,0.00002432471,0.0001453786],"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":[0.00001211569,0.00002608676,0.1774241,0.000003883724,0.00001258011,0.00002083003,0.004206918,0.7391872,0.00004958189,0.0003126333,0.0001780178,0.07856607],"study_design_scores_gemma":[0.0002895835,0.00003478444,0.01816913,0.00001977191,0.00001050849,0.000003267089,0.0000636601,0.9810802,0.0000197381,0.00007061092,0.0001174985,0.0001212804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4976957,0.00009691784,0.5009925,0.0002035461,0.0008242364,0.00008715399,6.570488e-7,0.00007581411,0.00002350673],"genre_scores_gemma":[0.9682612,0.00002583128,0.03100177,0.0001586024,0.0005340187,0.000006197187,0.000001514592,0.00000904546,0.000001814367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4705656,"threshold_uncertainty_score":0.5242238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1268723866010066,"score_gpt":0.3084885536545194,"score_spread":0.1816161670535128,"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."}}