{"id":"W7095411140","doi":"","title":"INFX 2600 – Text Matching, Statistics, Web Services, and Swine Flu","year":2009,"lang":"en","type":"article","venue":"","topic":"Vladimir Nabokov Literary Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Focus (optics); Relevance (law); Vetting; Cover (algebra); Informatics; Web page; Domain (mathematical analysis)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003472337,0.001115114,0.0006186762,0.002057901,0.001223014,0.0042462,0.0008402838,0.002066922,0.1288255],"category_scores_gemma":[0.00693559,0.0004531174,0.0006042053,0.002443072,0.001091659,0.005750369,0.00181047,0.001749805,0.05965191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003364573,"about_ca_system_score_gemma":0.001353661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007208921,"about_ca_topic_score_gemma":0.004291134,"domain_scores_codex":[0.9984541,0.0003667868,0.0001287247,0.0003433219,0.0005680727,0.0001390564],"domain_scores_gemma":[0.9980021,0.0008485366,0.0001100967,0.0002516983,0.0004398568,0.0003478005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001872665,0.00007261773,0.001127604,0.0001384069,0.00001272889,0.0001430743,0.0001908527,0.001274883,0.001509159,0.0680771,0.4882272,0.4390392],"study_design_scores_gemma":[0.00001587683,0.00009596309,0.002799824,0.0001475843,0.000004398451,0.0002739418,0.0001328087,0.005583871,0.001523281,0.03167422,0.9577267,0.00002166319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0105585,0.01411948,0.1185285,0.03247206,0.006822812,0.0003546232,0.005098837,0.02910938,0.7829357],"genre_scores_gemma":[0.06888706,0.01107792,0.09218736,0.007247513,0.005753241,0.000416988,0.01290382,0.007456115,0.7940699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1288255,"threshold_uncertainty_score":0.4309644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008299215150869633,"score_gpt":0.2806629003738252,"score_spread":0.2723636852229556,"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."}}