{"id":"W2390137859","doi":"","title":"Research and Implementation of Text Categorization System Based on VSM","year":2001,"lang":"en","type":"article","venue":"Jisuanji yingyong yanjiu","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Computer science; Categorization; Task (project management); Key (lock); Natural language processing; Text categorization; Artificial intelligence; Vector space model; Information extraction; Feature (linguistics); Space (punctuation); Information retrieval; Linguistics","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":[0.001471489,0.0003765829,0.000846218,0.002171553,0.0007261504,0.001023525,0.001793371,0.0008027286,0.004223221],"category_scores_gemma":[0.002541761,0.0002815821,0.0005349133,0.00184358,0.000314462,0.002467719,0.0005818928,0.0005264076,0.002622153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000988952,"about_ca_system_score_gemma":0.0008041296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002762137,"about_ca_topic_score_gemma":0.001451479,"domain_scores_codex":[0.9988738,0.00024929,0.00010081,0.0003117459,0.0003844537,0.0000798315],"domain_scores_gemma":[0.9989812,0.0002403659,0.00006863493,0.0001761799,0.0004743295,0.00005930219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000451166,0.0002360097,0.002717052,0.0004339069,0.0000971885,0.0002058957,0.0003316132,0.003809443,0.05887157,0.01299113,0.01755732,0.9022977],"study_design_scores_gemma":[0.0003060023,0.0009484171,0.008439323,0.0001782733,0.0002955688,0.001119292,0.0006030334,0.6200869,0.2235397,0.01934208,0.1249595,0.0001819586],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07137871,0.002312869,0.8839092,0.0007023215,0.000536753,0.0005384139,0.0009254743,0.03202936,0.007666904],"genre_scores_gemma":[0.3444803,0.0009167265,0.6373657,0.000354317,0.0002102926,0.0005494637,0.002816001,0.0003138911,0.01299337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004223221,"threshold_uncertainty_score":0.01412809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04949893093589035,"score_gpt":0.4036949908625966,"score_spread":0.3541960599267062,"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."}}