{"id":"W1481561381","doi":"10.1007/978-1-4899-7993-3_949-2","title":"Relevance Feedback for Text Retrieval","year":2016,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":0,"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":[0.001482872,0.0009677999,0.001498678,0.001585136,0.0006147838,0.00204098,0.001601347,0.001569542,0.02062283],"category_scores_gemma":[0.006539321,0.0004232974,0.000576556,0.002253244,0.0008619871,0.003016813,0.001248205,0.001257727,0.01467677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009104845,"about_ca_system_score_gemma":0.0007621312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002327536,"about_ca_topic_score_gemma":0.002965888,"domain_scores_codex":[0.9977353,0.0007592899,0.0001388141,0.0003144812,0.0009478641,0.0001043715],"domain_scores_gemma":[0.9983565,0.0009134692,0.00006794163,0.0002827361,0.0003454437,0.00003384123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001217294,0.00007695198,0.00009263169,0.0008117405,0.00003909877,0.00006689933,0.0001098585,0.007136606,0.005413593,0.04533471,0.11635,0.8244462],"study_design_scores_gemma":[0.0001145679,0.0003009965,0.00113658,0.0004091147,0.0001236355,0.0009147865,0.0001370749,0.3126583,0.0155637,0.3621258,0.3063636,0.0001517484],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005189309,0.08156478,0.8603063,0.002395744,0.00188319,0.0002648502,0.0008342271,0.005718751,0.04184294],"genre_scores_gemma":[0.2171524,0.0367176,0.575973,0.001203756,0.004299967,0.0005551624,0.003863107,0.001209561,0.1590255],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02062283,"threshold_uncertainty_score":0.06899023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02340170619147969,"score_gpt":0.2573843107632072,"score_spread":0.2339826045717275,"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."}}