{"id":"W2009665825","doi":"10.1145/971617.971644","title":"Proactive information gathering for homeland security teams","year":2004,"lang":"en","type":"article","venue":"Communications of the ACM","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Homeland security; Computer security; Computer science; Information security; Homeland; Internet privacy; Terrorism; Political science","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":[0.002052268,0.000464759,0.0002662095,0.0006726707,0.002197827,0.002939764,0.0009348485,0.001367765,0.00583926],"category_scores_gemma":[0.007355186,0.0004279292,0.0002141692,0.0003845433,0.0003924423,0.003313988,0.003048157,0.001278473,0.001145191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006858226,"about_ca_system_score_gemma":0.002054618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003630982,"about_ca_topic_score_gemma":0.005686136,"domain_scores_codex":[0.9988559,0.0006388882,0.0000518154,0.0001155376,0.0001974512,0.000140351],"domain_scores_gemma":[0.9969679,0.001317314,0.0003703033,0.000391711,0.0003987916,0.0005541068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002599003,0.001795598,0.01473774,0.0006925896,0.0001684517,0.001130511,0.01637876,0.03494715,0.03519231,0.05219604,0.06191629,0.7782456],"study_design_scores_gemma":[0.0007507579,0.002016588,0.01233864,0.0005737549,0.0003276461,0.0009563545,0.03561287,0.6197346,0.03513936,0.1133714,0.178936,0.0002420343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4909999,0.002017138,0.3782701,0.01374315,0.0005999312,0.001058355,0.0003866088,0.007790934,0.1051338],"genre_scores_gemma":[0.9174692,0.0002560135,0.07651646,0.0001658551,0.00005096578,0.0001859875,0.0002282719,0.00006793776,0.0050593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00583926,"threshold_uncertainty_score":0.01953429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.027105491335526,"score_gpt":0.2751920754485727,"score_spread":0.2480865841130467,"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."}}