{"id":"W1550901011","doi":"10.1002/9780470176535.ch3","title":"Feature Extraction, Selection, and Creation","year":2007,"lang":"en","type":"other","venue":"","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Selection (genetic algorithm); Computer science; Feature selection; Feature (linguistics); Artificial intelligence","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":[0.00007045269,0.0001165245,0.0000896666,0.0004862679,0.000050062,0.0001306804,0.0002270704,0.0003110262,0.0004434692],"category_scores_gemma":[0.0000175874,0.00009922779,0.00001737179,0.00035967,0.00002803208,0.0001561629,0.0000500997,0.0001356187,0.0001070601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002338075,"about_ca_system_score_gemma":0.00001917536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002592087,"about_ca_topic_score_gemma":0.00006527031,"domain_scores_codex":[0.999405,0.000008426568,0.0000663902,0.0002875493,0.000126483,0.0001060961],"domain_scores_gemma":[0.999563,0.00001867828,0.0001065474,0.0002530302,0.00003120939,0.00002747771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[2.903961e-7,0.000006831201,0.0001077966,0.000005365835,0.000006606573,3.638072e-7,0.000006433697,5.447778e-8,0.00003757266,0.1566339,0.7863204,0.05687434],"study_design_scores_gemma":[0.00005817825,0.00001311262,0.000476943,0.00001108063,0.000003431318,0.0000119812,0.00001065604,0.0002129674,0.0005169229,0.001346512,0.997218,0.0001201869],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[7.688662e-7,0.0003570326,0.4777219,0.001833922,0.0001294582,0.0000816672,3.766297e-7,0.001661434,0.5182135],"genre_scores_gemma":[0.0002286589,0.0002727475,0.06899961,0.00009810454,0.00008045053,0.000008794945,0.000004855967,0.00003954319,0.9302672],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.4120538,"threshold_uncertainty_score":0.4855677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01026283655632646,"score_gpt":0.2800368526749832,"score_spread":0.2697740161186568,"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."}}