{"id":"W2171484504","doi":"10.5210/ojphi.v3i2.3607","title":"Improving Agent Based Models and Validation through Data Fusion","year":2011,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Orthopaedic Innovation Centre","funders":"","keywords":"Computer science; Data science; Bluetooth; Population; Granularity; Data mining; Agent-based model; Sensor fusion; Novelty; Robustness (evolution); Data aggregator; Machine learning; Artificial intelligence; Telecommunications; Wireless; Medicine; Wireless sensor network; Computer network","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":[0.02941766,0.002012831,0.002657225,0.002237237,0.001536797,0.00413561,0.003052444,0.002974113,0.001585672],"category_scores_gemma":[0.08185017,0.001754234,0.002099871,0.001600503,0.001812585,0.004420613,0.00493197,0.004029445,0.0004605343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001883113,"about_ca_system_score_gemma":0.003647721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01695268,"about_ca_topic_score_gemma":0.00694636,"domain_scores_codex":[0.9881732,0.007738918,0.0009851474,0.001244429,0.001459398,0.000398914],"domain_scores_gemma":[0.9319696,0.05121223,0.003996251,0.006579781,0.005537261,0.000704888],"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":[0.00008892093,0.00004139918,0.002680907,0.00004636975,0.0001096985,0.00004124498,0.00008633253,0.9817406,0.0002580261,0.003896935,0.0002004516,0.01080906],"study_design_scores_gemma":[0.000009030555,0.00001498802,0.0001164402,0.000009066936,0.000007478437,0.00000487127,0.00001082174,0.9950315,0.000182758,0.004499002,0.0001085971,0.000005573733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03877487,0.0002659418,0.9578856,0.0006990278,0.00007448602,0.0001492673,0.0002459121,0.0009025621,0.001002329],"genre_scores_gemma":[0.7484978,0.0001988305,0.2490958,0.0002721921,0.00005412792,0.0002782535,0.0008348327,0.0001255187,0.0006424874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02941766,"threshold_uncertainty_score":0.1555774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7488719034865434,"score_gpt":0.4974264133979003,"score_spread":0.2514454900886431,"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."}}