{"id":"W4384659754","doi":"10.1145/3539618.3592045","title":"Quantifying Ranker Coverage of Different Query Subspaces","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Computer science; Ranking (information retrieval); Metric (unit); Task (project management); Learning to rank; Linear subspace; Machine learning; Range (aeronautics); Subspace topology; Artificial intelligence; Data mining; Information retrieval; Performance metric; Rank (graph theory); Mathematics","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":[0.007668686,0.001230894,0.001694548,0.00442702,0.0005958717,0.002069869,0.0007681604,0.001273953,0.001604693],"category_scores_gemma":[0.03775929,0.0002474022,0.0005932794,0.003027652,0.001358957,0.003895748,0.002281728,0.001118669,0.00115441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005845875,"about_ca_system_score_gemma":0.0008491805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002664807,"about_ca_topic_score_gemma":0.003547504,"domain_scores_codex":[0.9935161,0.002038146,0.0006229915,0.0009744217,0.002237968,0.0006103684],"domain_scores_gemma":[0.9747503,0.01673242,0.002086271,0.003238777,0.002430143,0.0007620212],"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":[0.003082165,0.0007777792,0.1014746,0.001604466,0.0009016311,0.0004288794,0.001297287,0.2254338,0.07512209,0.005874139,0.01072337,0.5732798],"study_design_scores_gemma":[0.0001286578,0.003941673,0.09582117,0.000126526,0.000488963,0.001540131,0.001517878,0.7818687,0.08923601,0.01628127,0.00877929,0.0002698317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8502152,0.007340658,0.125093,0.0006445831,0.0001286592,0.0002546101,0.002529539,0.003014658,0.01077917],"genre_scores_gemma":[0.9658239,0.0007150014,0.02894828,0.0001212146,0.0001158882,0.00007688903,0.002719594,0.0002423817,0.001236643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007668686,"threshold_uncertainty_score":0.04055637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05048798549562439,"score_gpt":0.3258476472298028,"score_spread":0.2753596617341784,"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."}}