{"id":"W2141501959","doi":"10.1109/icdew.2007.4401067","title":"Incorporating Temporal Information for Document Classification","year":2007,"lang":"en","type":"article","venue":"","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Document classification; Feature selection; Artificial intelligence; Feature (linguistics); Set (abstract data type); Genetic programming; Sequence (biology); Word (group theory); Selection (genetic algorithm); Data mining; Pattern recognition (psychology); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0010135,0.0004695555,0.0004821107,0.0018219,0.0004252813,0.0009183725,0.0007663132,0.0006374272,0.001234179],"category_scores_gemma":[0.00309306,0.0001751868,0.0005842925,0.002086881,0.0003018519,0.002050339,0.0004293171,0.0007023216,0.0009928079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004896265,"about_ca_system_score_gemma":0.0006565127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003311058,"about_ca_topic_score_gemma":0.004489636,"domain_scores_codex":[0.9993999,0.0001229625,0.00006451324,0.0001545145,0.0002052382,0.00005276174],"domain_scores_gemma":[0.9988176,0.0004413136,0.0001462998,0.0001443515,0.0004076934,0.00004268678],"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":[0.0001756665,0.000165353,0.002523162,0.0001807726,0.00007885328,0.0001103012,0.0001085272,0.03127975,0.02779569,0.00515334,0.003011848,0.9294167],"study_design_scores_gemma":[0.00001639908,0.0001545356,0.001763577,0.00004123991,0.0001080107,0.0001924324,0.00005453396,0.9667497,0.01554559,0.007491576,0.007842293,0.00004013558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04110854,0.001317542,0.9517075,0.0002894374,0.000230303,0.0001088452,0.0003249868,0.002180184,0.002732679],"genre_scores_gemma":[0.4424192,0.001206038,0.5501705,0.0001913475,0.0003587825,0.0001784675,0.001137023,0.0001610207,0.004177568],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003311058,"threshold_uncertainty_score":0.006583571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02593978452145998,"score_gpt":0.2846569852707369,"score_spread":0.2587172007492769,"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."}}