{"id":"W1594494575","doi":"10.1007/3-540-39963-1_47","title":"Automatic Semantic Header Generator","year":2000,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Search engine indexing; Computer science; Header; Information retrieval; Automatic indexing; Resource (disambiguation); Information extraction; The Internet; Context (archaeology); Generator (circuit theory); Data mining; World Wide Web","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":[0.0005892966,0.0006332298,0.000719027,0.0008248429,0.0002793017,0.0007471789,0.004146605,0.0003873863,0.0001620912],"category_scores_gemma":[0.00006568905,0.0005401691,0.0001734357,0.0005919751,0.0007390995,0.0005872212,0.0009095159,0.0006905714,0.0003284572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002055016,"about_ca_system_score_gemma":0.000652238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003256484,"about_ca_topic_score_gemma":0.00008291741,"domain_scores_codex":[0.9957349,0.00004204221,0.000616402,0.001665969,0.001053049,0.0008876228],"domain_scores_gemma":[0.9971558,0.000394993,0.0002133099,0.001917547,0.0001252981,0.000192997],"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":[0.000001772823,0.00002884787,0.00004813766,0.00005396205,0.00001583488,0.0002535809,0.0006597374,0.004982969,0.00005893032,0.01626039,0.0001351145,0.9775007],"study_design_scores_gemma":[0.0002560138,0.0001376883,0.0003035598,0.0004221462,0.00001332167,0.0002279393,1.639469e-7,0.7846426,0.0009220798,0.2090973,0.003084653,0.0008925132],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000596477,0.001199677,0.9876993,0.001478952,0.002642327,0.0003548956,0.000002127445,0.0004937564,0.005532438],"genre_scores_gemma":[0.2369386,0.0001963676,0.7536695,0.006604369,0.001444086,0.00002132036,0.000005756876,0.00008212739,0.001037931],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9766082,"threshold_uncertainty_score":0.999705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02005951519168582,"score_gpt":0.2453746573692003,"score_spread":0.2253151421775145,"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."}}