{"id":"W2160938081","doi":"10.1017/s1351324912000289","title":"Modeling human newspaper readers: The Fuzzy Believer approach","year":2012,"lang":"en","type":"article","venue":"Natural Language Engineering","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Newspaper; Computer science; Fuzzy logic; Set (abstract data type); Artificial intelligence; Range (aeronautics); Natural (archaeology); Fuzzy set; Information extraction; Natural language processing; Information retrieval; Data mining; Advertising; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002933752,0.0001645725,0.0001212013,0.00006507017,0.0001184482,0.0001014515,0.0007244508,0.00007234208,0.000004749652],"category_scores_gemma":[0.00004116762,0.0001151248,0.00006953281,0.0001947543,0.000008337685,0.0006675781,0.0002168234,0.0004437825,0.0000147378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005122065,"about_ca_system_score_gemma":0.000007597086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008474699,"about_ca_topic_score_gemma":0.000002181724,"domain_scores_codex":[0.9988541,0.00002024837,0.0001644239,0.0002266583,0.000261593,0.0004729599],"domain_scores_gemma":[0.9992811,0.00003175833,0.00002209408,0.0005597812,0.00002155429,0.00008369671],"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":[0.000002366242,0.00006254878,0.0001682238,0.00009242136,0.00008379867,0.00001427842,0.04327936,0.7974315,0.02997349,0.09644469,0.0002462217,0.03220113],"study_design_scores_gemma":[0.0000998709,0.000002938483,0.0001011122,0.00001312241,0.000005617579,0.00002578001,0.0003956633,0.9985731,0.0003408999,0.00002313013,0.0002333487,0.0001853791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1533765,0.00772042,0.8356439,0.0002151037,0.0007729686,0.000152949,4.695337e-7,0.0004773224,0.0016404],"genre_scores_gemma":[0.9056759,0.000002238501,0.09325685,0.0001965847,0.0006147677,0.000011132,0.000003408087,0.00001760693,0.000221527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7522994,"threshold_uncertainty_score":0.4694655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539149631651612,"score_gpt":0.2320307436379919,"score_spread":0.2166392473214758,"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."}}