{"id":"W3125108001","doi":"10.5334/sta.cr","title":"Machine Learning and Conflict Prediction: A Use Case","year":2013,"lang":"en","type":"article","venue":"Stability International Journal of Security and Development","topic":"Political Conflict and Governance","field":"Social Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machine learning; Warning system; Computer science; Artificial intelligence; Field (mathematics); Predictive power; Selection (genetic algorithm); Test (biology); Early warning system; Risk analysis (engineering)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006309404,0.00006772909,0.0001128814,0.00004410004,0.0002140016,0.000190512,0.00008256446,0.00004797188,0.0003865752],"category_scores_gemma":[0.0006352365,0.00005924479,0.0000237543,0.00003367734,0.0002202232,0.0005624726,0.00006064013,0.0002071419,0.000003503785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001069427,"about_ca_system_score_gemma":0.0001760511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00218095,"about_ca_topic_score_gemma":0.000556131,"domain_scores_codex":[0.9989699,0.00009499386,0.0003069754,0.00009454873,0.0003958832,0.0001376594],"domain_scores_gemma":[0.9989169,0.0002224104,0.0001350089,0.00002764818,0.0004986165,0.0001994437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001289339,0.0002421642,0.7079216,0.00003931507,0.0002409665,0.0003863874,0.2434688,0.000001224096,0.00007784446,0.01914149,0.0001345551,0.02821676],"study_design_scores_gemma":[0.001160721,0.0001186017,0.1011345,0.00007962369,0.00001599273,0.0009952976,0.02426746,0.0001864565,0.0002693224,0.003395482,0.8681419,0.0002346484],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959185,0.0003731067,0.00009179137,0.001776177,0.0002063574,0.00008574296,0.0000151189,0.00001004146,0.001523165],"genre_scores_gemma":[0.9985747,0.0005068667,0.0005357036,0.0001662309,0.0001232639,0.000002530174,0.000001677258,0.00000240333,0.00008663214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8680073,"threshold_uncertainty_score":0.4232728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03882805782316824,"score_gpt":0.3056701606244451,"score_spread":0.2668421028012768,"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."}}