{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001470819,0.00009385615,0.0001635665,0.0001299975,0.00004467148,0.0000437218,0.0003998875,0.0000333376,0.00001986006],"category_scores_gemma":[0.00005314605,0.00006941069,0.00006777098,0.0005601273,0.00002791889,0.0004275998,0.000242376,0.0000674894,0.00003982816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001092659,"about_ca_system_score_gemma":0.00001288594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001351639,"about_ca_topic_score_gemma":0.000006864642,"domain_scores_codex":[0.9991915,0.00002873932,0.0001724101,0.0002105332,0.000206279,0.0001905006],"domain_scores_gemma":[0.9993449,0.0001467631,0.00006069055,0.0003643729,0.00004399215,0.00003925682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003728484,0.0001841206,0.0313969,0.0002209553,0.00006407033,0.000107329,0.001275115,0.00005007859,0.1722076,0.3682249,0.01011961,0.416112],"study_design_scores_gemma":[0.0002777628,0.0001216717,0.01992757,0.00005621561,0.000004097946,0.000004287993,0.00005180941,0.008059204,0.9462934,0.02175514,0.003201886,0.0002469531],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08398119,0.00007868887,0.9120438,0.0002952635,0.0001126038,0.00009670133,0.000001012321,0.0007524716,0.002638325],"genre_scores_gemma":[0.9867082,0.0005090731,0.01164819,0.0001136037,0.00001592354,0.000006147538,0.000001669933,0.000006904959,0.0009902631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.902727,"threshold_uncertainty_score":0.2830486,"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."}}