{"id":"W2318083464","doi":"10.1109/wartia.2014.6976179","title":"Quantifying risk for determining optimal joint fires in defence planning","year":2014,"lang":"en","type":"article","venue":"2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)","topic":"Military Defense Systems Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Ammunition; Collateral; Collateral damage; Joint (building); Computer science; Risk analysis (engineering); Task (project management); Operations research; Engineering; Systems engineering; Business; Civil 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":[0.001302103,0.0002146872,0.0003677004,0.001523487,0.000278016,0.00003411054,0.0003342824,0.0006984067,0.00000750203],"category_scores_gemma":[0.0005002568,0.000231934,0.00003876932,0.001658197,0.0002722992,0.0001312606,0.00007111825,0.002271647,0.0000256365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001321364,"about_ca_system_score_gemma":0.00002593206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001898636,"about_ca_topic_score_gemma":0.0001658704,"domain_scores_codex":[0.9978478,0.00009342917,0.0005130014,0.0005851447,0.0002139707,0.0007466483],"domain_scores_gemma":[0.9982178,0.0008146575,0.00007393867,0.0006837021,0.00009701748,0.0001128464],"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":[0.00005215227,0.0001094806,0.03987385,0.0001662975,0.00004947892,0.0000130808,0.0002562237,0.8161343,0.01119507,0.003008127,0.0006791159,0.1284628],"study_design_scores_gemma":[0.001951071,0.0003327843,0.006424787,0.001248656,0.00002268125,0.00003116577,0.004999732,0.9483551,0.01786626,0.008464334,0.009413819,0.0008895595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9284403,0.0009202883,0.06880233,0.0003686081,0.00005269911,0.0008995718,0.00001312226,0.0002625472,0.0002405492],"genre_scores_gemma":[0.9889699,0.0002274198,0.008051291,0.000008241817,0.00006170986,0.002579092,0.000007728738,0.00004027041,0.00005438042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1322208,"threshold_uncertainty_score":0.9869303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06920409285081681,"score_gpt":0.3581107064152202,"score_spread":0.2889066135644034,"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."}}