{"id":"W1559186764","doi":"10.1007/978-3-540-45091-7_26","title":"Sesei: A CG-Based Filter for Internet Search Engines","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; The Internet; Filter (signal processing); Search engine; World Wide Web; Information retrieval; Artificial intelligence; Speech recognition; Computer vision","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001118271,0.000602135,0.0005365536,0.001076773,0.0001607722,0.0008314633,0.004427923,0.0004176644,0.0000296656],"category_scores_gemma":[0.0002216167,0.0005158362,0.0001935267,0.0006152784,0.0005037886,0.0005574267,0.000877713,0.0008990323,0.00001582816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003206499,"about_ca_system_score_gemma":0.0005838019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001528713,"about_ca_topic_score_gemma":0.00002329737,"domain_scores_codex":[0.9960949,0.00004250284,0.0004671726,0.001671001,0.0008962136,0.000828209],"domain_scores_gemma":[0.9971011,0.0007496743,0.0001943867,0.001295605,0.0004913466,0.0001678423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000211762,0.00005368685,0.00002155126,0.0003039048,0.00002148941,0.0001283213,0.0007196529,0.005495905,0.0005988484,0.0735098,0.0007244969,0.9184012],"study_design_scores_gemma":[0.0003414922,0.0003054053,0.000003390024,0.0007036717,0.000009583399,0.00006203026,6.438044e-8,0.7191354,0.04762593,0.2261237,0.004771453,0.0009178122],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000008260286,0.001672707,0.9942387,0.001401817,0.001040455,0.0006958576,0.00001287672,0.0004742868,0.0004550259],"genre_scores_gemma":[0.02318406,0.000006959784,0.9712971,0.004299871,0.0002723195,0.00003744902,0.00000863581,0.0000519512,0.0008416597],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9174833,"threshold_uncertainty_score":0.9997293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02244949489369179,"score_gpt":0.2802579448231724,"score_spread":0.2578084499294806,"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."}}