{"id":"W2029583420","doi":"10.1109/wi.2004.80","title":"Improving Efficiency and Relevance Ranking in Information Retrieval","year":2004,"lang":"en","type":"article","venue":"","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Ranking (information retrieval); Relevance (law); Computer science; Similarity (geometry); Computation; Information retrieval; The Internet; Data mining; Ranking SVM; Learning to rank; Artificial intelligence; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004274966,0.00006393594,0.00006793322,0.000174574,0.00008627256,0.0002078111,0.0002314093,0.00003803555,0.000004303991],"category_scores_gemma":[0.0001976584,0.00005406353,0.00001475088,0.0005272739,0.00002432241,0.003363615,0.000121886,0.0001200691,0.00004196526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006725085,"about_ca_system_score_gemma":0.00007929588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007727514,"about_ca_topic_score_gemma":0.000005616536,"domain_scores_codex":[0.9991812,0.00001038727,0.0002484914,0.00009378655,0.0002689036,0.0001971908],"domain_scores_gemma":[0.9996443,0.00003920708,0.00005577505,0.0001447572,0.00006812403,0.00004779435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006759028,0.00008355911,0.001825685,0.000120662,0.000002802195,0.00001692953,0.0130056,0.003791152,0.003476263,0.5041634,0.00002152072,0.4734248],"study_design_scores_gemma":[0.01045458,0.0007550235,0.05625323,0.0002105747,0.000006901058,0.0001755129,0.0009300669,0.8511583,0.05968531,0.01426224,0.004757725,0.001350559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.258165,0.00001839976,0.7392952,0.0003526489,0.00008735057,0.0001402425,3.398637e-7,0.00008397305,0.001856783],"genre_scores_gemma":[0.9835953,0.00001399506,0.01602767,0.0003183091,0.000007160903,0.000001957302,0.000001149457,0.000001333521,0.00003311034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8473671,"threshold_uncertainty_score":0.243854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007935231399434549,"score_gpt":0.226684511014785,"score_spread":0.2187492796153504,"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."}}