{"id":"W2545281775","doi":"10.1109/greencom-cpscom.2010.13","title":"Human Contact Prediction Using Contact Graph Inference","year":2010,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Homophily; Inference; Computer science; Graph; Theoretical computer science; Artificial intelligence; Machine learning; Mathematics","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":[0.0007725235,0.0008153994,0.0008798447,0.002531391,0.0005631775,0.0007955726,0.001206954,0.001471751,0.001567191],"category_scores_gemma":[0.007543938,0.0004424517,0.000551335,0.001772009,0.0005750695,0.002466882,0.0007704066,0.001017327,0.0007209325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005008727,"about_ca_system_score_gemma":0.0004594969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004711195,"about_ca_topic_score_gemma":0.005536929,"domain_scores_codex":[0.9992478,0.0002615263,0.00003581678,0.0002723183,0.0001182488,0.00006429994],"domain_scores_gemma":[0.9953951,0.003069005,0.0005689279,0.000488723,0.0003174786,0.0001606087],"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":[0.0003928517,0.0003933097,0.07927813,0.0002890125,0.0003191599,0.0005578715,0.00027369,0.7061529,0.005059084,0.01590437,0.007394753,0.1839849],"study_design_scores_gemma":[0.000005912366,0.00001842537,0.0031046,0.000006919399,0.00001435899,0.00005739582,0.0000388917,0.9826806,0.0006955998,0.01300204,0.000368219,0.000006910101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2198825,0.0005040727,0.7732271,0.000670017,0.00005617331,0.0001034764,0.001550999,0.001110187,0.002895456],"genre_scores_gemma":[0.946748,0.0002078467,0.05066762,0.000046455,0.00005253288,0.00004408946,0.001406715,0.00003862112,0.0007880981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004711195,"threshold_uncertainty_score":0.009367526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03565260061560722,"score_gpt":0.3489213482453695,"score_spread":0.3132687476297623,"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."}}