{"id":"W2919722095","doi":"10.1007/978-1-4614-8265-9_949","title":"Relevance Feedback for Text Retrieval","year":2018,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Relevance feedback; Relevance (law); Information retrieval; Computer science; Artificial intelligence; Image retrieval; Political science","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":[0.001517086,0.0009961425,0.001511907,0.001654228,0.0006243661,0.001996615,0.001657765,0.00161668,0.02082729],"category_scores_gemma":[0.006498218,0.0004313705,0.0006111134,0.00231482,0.0008745631,0.003117039,0.001227965,0.001348941,0.01514193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009698786,"about_ca_system_score_gemma":0.0007881269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002238049,"about_ca_topic_score_gemma":0.002669177,"domain_scores_codex":[0.997705,0.0007485842,0.0001383117,0.0003297405,0.0009635497,0.0001147839],"domain_scores_gemma":[0.9982399,0.0009673398,0.00007115791,0.0003006106,0.0003833792,0.00003755972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001291649,0.00008741876,0.00008991634,0.0009146559,0.00004123342,0.00006733401,0.0001078278,0.007067135,0.005946701,0.04424755,0.1208322,0.8204688],"study_design_scores_gemma":[0.0001195608,0.0003366383,0.001167826,0.0004399474,0.000133145,0.0009058678,0.0001355079,0.3156519,0.01734849,0.3406411,0.3229648,0.0001551191],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005482088,0.08933654,0.850835,0.002593423,0.002106175,0.0002817741,0.000877011,0.006484711,0.04200326],"genre_scores_gemma":[0.2170666,0.03841281,0.5647124,0.001355558,0.004669853,0.000566229,0.003977615,0.001269145,0.1679698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02082729,"threshold_uncertainty_score":0.06967425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02600851367421128,"score_gpt":0.2635334445071876,"score_spread":0.2375249308329763,"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."}}