{"id":"W4366967218","doi":"10.1109/wi-iat55865.2022.00141","title":"A Multi-Dimensional Semantic Pseudo-Relevance Feedback Information Retrieval Model","year":2022,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Information retrieval; Ranking (information retrieval); Paragraph; Relevance (law); Relevance feedback; Sentence; Semantic similarity; Polysemy; Concept search; Word (group theory); Artificial intelligence; Natural language processing; Image retrieval; Search engine; Web search query; World Wide Web","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":[0.0024789,0.001036539,0.001586653,0.001885991,0.0006719173,0.001413736,0.002758024,0.001601098,0.003696275],"category_scores_gemma":[0.004466234,0.0004985943,0.001104675,0.002367705,0.0008092124,0.004020213,0.0009385965,0.001049212,0.001499563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001452776,"about_ca_system_score_gemma":0.001188131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005286105,"about_ca_topic_score_gemma":0.003839769,"domain_scores_codex":[0.9975365,0.000817796,0.0001685351,0.0004864609,0.0008123905,0.0001782474],"domain_scores_gemma":[0.9985489,0.0005324232,0.0001458885,0.0001321355,0.0005832404,0.00005732542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007373163,0.0004996674,0.002985576,0.00079803,0.0002652587,0.0006189434,0.0007229884,0.5402809,0.01398215,0.1203638,0.01368679,0.3050584],"study_design_scores_gemma":[0.00004272164,0.00009254886,0.0002577978,0.000008356186,0.00003398801,0.0001210161,0.00001464974,0.9801855,0.0006440718,0.01719216,0.001383384,0.00002384407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03062407,0.001728811,0.9587205,0.00112685,0.0001529224,0.0002263249,0.000402524,0.001178139,0.005839847],"genre_scores_gemma":[0.8248425,0.001537001,0.1565038,0.000460179,0.0003575312,0.0005704606,0.0007765184,0.0001005529,0.01485157],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005286105,"threshold_uncertainty_score":0.0131098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02305350140983097,"score_gpt":0.2412059020229618,"score_spread":0.2181524006131308,"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."}}