{"id":"W4382753054","doi":"10.3390/e25071011","title":"Modeling Terror Attacks with Self-Exciting Point Processes and Forecasting the Number of Terror Events","year":2023,"lang":"en","type":"article","venue":"Entropy","topic":"Point processes and geometric inequalities","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Terrorism; Computer science; Order (exchange); Process (computing); Event (particle physics); Point process; Point (geometry); Model selection; Computer security; Artificial intelligence; Political science; Economics; Statistics; Mathematics; Law","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005334887,0.0001556912,0.0002476689,0.00009281153,0.00016295,0.00003579672,0.0001458518,0.00004063742,0.00003113614],"category_scores_gemma":[0.000952383,0.00009399576,0.00003802658,0.0006298046,0.00003066759,0.0001709276,0.000108089,0.0001182693,0.000008189013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001781131,"about_ca_system_score_gemma":0.00005458698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000308361,"about_ca_topic_score_gemma":0.00001495996,"domain_scores_codex":[0.9987568,0.00003521465,0.0003765225,0.000194954,0.0003158965,0.0003206533],"domain_scores_gemma":[0.9990295,0.0003833886,0.0001941462,0.0001698782,0.0001811515,0.00004197664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00147348,0.002023748,0.2981726,0.07382883,0.003442832,0.0002614351,0.3142323,0.01097665,0.00169435,0.26649,0.004263577,0.0231401],"study_design_scores_gemma":[0.004201377,0.000424895,0.0008366053,0.002904659,0.0004513313,0.00048725,0.07934679,0.5344871,0.003330333,0.3717429,0.0003795349,0.001407316],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891345,0.00006480837,0.009228003,0.0004147551,0.00003958649,0.0002080197,0.00001172449,0.0001460232,0.0007525744],"genre_scores_gemma":[0.9921966,0.00001913413,0.007211656,0.00003909936,0.00009103864,0.00003442723,0.000004616231,0.00003169638,0.0003717662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5235104,"threshold_uncertainty_score":0.3833036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07405894924746974,"score_gpt":0.3222719515320732,"score_spread":0.2482130022846034,"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."}}