{"id":"W4413982584","doi":"10.1007/s10115-025-02581-5","title":"A knowledge-based approach for pseudo-relevance feedback by exploiting semantic relevance","year":2025,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Relevance (law); Relevance feedback; Computer science; Information retrieval; 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":[0.004035518,0.000909107,0.001507434,0.002939938,0.001197161,0.002194743,0.003327083,0.002241441,0.006358841],"category_scores_gemma":[0.01859672,0.00060299,0.001111595,0.002091022,0.001217726,0.005986033,0.002557399,0.001836829,0.002164808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060511,"about_ca_system_score_gemma":0.002155143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004033792,"about_ca_topic_score_gemma":0.006513511,"domain_scores_codex":[0.9940546,0.002089141,0.0003204862,0.0007820238,0.002471671,0.0002820176],"domain_scores_gemma":[0.9901529,0.00482697,0.0003166739,0.001687288,0.002777749,0.0002384827],"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":[0.001003328,0.001033743,0.001264136,0.000672198,0.000225862,0.0003585306,0.0008207597,0.06381314,0.03595321,0.1121385,0.01433505,0.7683815],"study_design_scores_gemma":[0.00007013977,0.0001625864,0.0003875243,0.00003690717,0.00008076789,0.0002053433,0.00006746743,0.9124602,0.009435197,0.07198302,0.005037846,0.00007292388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006107633,0.0003299564,0.9896514,0.0002791254,0.00009584036,0.0001537349,0.00009989125,0.0009080844,0.002374254],"genre_scores_gemma":[0.4195779,0.0003454488,0.5713725,0.000358886,0.0002896092,0.0003708983,0.0004649305,0.0002630299,0.006956795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006358841,"threshold_uncertainty_score":0.0213421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01988378067934126,"score_gpt":0.2714622971701461,"score_spread":0.2515785164908048,"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."}}