{"id":"W213460862","doi":"10.5220/0002716800450052","title":"ON USING SIMULATION AND STOCHASTIC LEARNING FOR PATTERN RECOGNITION WHEN TRAINING DATA IS UNAVAILABLE - The Case of Disease Outbreak","year":2010,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Training (meteorology); Outbreak; Machine learning; Artificial intelligence; Training set; Data modeling; Disease; Pattern recognition (psychology); Medicine; Database; Geography","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":[0.006402456,0.0006358018,0.001530221,0.001352243,0.0006815045,0.001582167,0.001510583,0.002330731,0.00165794],"category_scores_gemma":[0.04603221,0.0006046544,0.0008658,0.001067623,0.002555386,0.003860489,0.001781261,0.001627696,0.0002325815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001111168,"about_ca_system_score_gemma":0.001233514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007541257,"about_ca_topic_score_gemma":0.004825177,"domain_scores_codex":[0.998412,0.0009305095,0.0001185245,0.0001812382,0.000252382,0.0001052002],"domain_scores_gemma":[0.9432226,0.05149887,0.001191598,0.002164316,0.001555631,0.0003669278],"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":[0.0001554792,0.00005310572,0.002414754,0.00004132113,0.00005210097,0.00009143088,0.00007526927,0.9415393,0.0003963266,0.02418509,0.0005224803,0.03047334],"study_design_scores_gemma":[0.000003852095,0.000006106678,0.00006630307,0.000003230739,0.000002328055,0.00001542972,0.0000043486,0.9911595,0.0001185323,0.008547131,0.00007027433,0.000003054223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05162309,0.000556918,0.9442473,0.001472307,0.00006211136,0.00005117085,0.00004337139,0.0003132281,0.001630515],"genre_scores_gemma":[0.7967296,0.0007639202,0.1998252,0.0003726755,0.0001488015,0.00012839,0.0001657783,0.0001096979,0.001755987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007541257,"threshold_uncertainty_score":0.03385985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.125765900461168,"score_gpt":0.3308350851122124,"score_spread":0.2050691846510443,"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."}}