{"id":"W2062626555","doi":"10.1007/s10791-009-9105-0","title":"Swapping documents and terms","year":2009,"lang":"en","type":"article","venue":"Information Retrieval","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Agricultural Research Development Agency","keywords":"Relevance feedback; Computer science; Information retrieval; Relevance (law); Query expansion; Data mining; Artificial intelligence; Image retrieval","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":[0.002680653,0.00066347,0.0009477152,0.002735638,0.000890645,0.003296962,0.001478037,0.001267794,0.02137091],"category_scores_gemma":[0.02593761,0.000512286,0.0008229727,0.004271405,0.001002904,0.006873904,0.002299457,0.001267288,0.008488594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000513286,"about_ca_system_score_gemma":0.001160639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009419817,"about_ca_topic_score_gemma":0.0009291171,"domain_scores_codex":[0.9963199,0.001280088,0.0002848349,0.0006523844,0.001145825,0.0003170098],"domain_scores_gemma":[0.9886584,0.004642736,0.0003853435,0.004230336,0.001660075,0.0004231975],"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":[0.002706379,0.0009855072,0.008662329,0.0006480611,0.0001794524,0.0003377571,0.001124324,0.003946127,0.1050975,0.03756563,0.01612337,0.8226236],"study_design_scores_gemma":[0.0007014805,0.002488877,0.02795899,0.0002971346,0.001100969,0.005070894,0.003291498,0.135879,0.3973177,0.2002103,0.22529,0.0003931457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6505876,0.00506926,0.2637179,0.00303477,0.002099838,0.001109968,0.004336834,0.009796022,0.06024775],"genre_scores_gemma":[0.8401654,0.001432168,0.1236607,0.0006602156,0.000320081,0.0002839027,0.003221756,0.00109768,0.02915794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02137091,"threshold_uncertainty_score":0.07149285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009581800700385145,"score_gpt":0.2530781925278839,"score_spread":0.2434963918274988,"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."}}