{"id":"W2767929746","doi":"10.1145/3132847.3133138","title":"An Empirical Study of Embedding Features in Learning to Rank","year":2017,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Toronto Metropolitan University","funders":"","keywords":"Embedding; Computer science; Rank (graph theory); Ranking (information retrieval); Learning to rank; Word embedding; Artificial intelligence; Word (group theory); Information retrieval; Empirical research; Machine learning; Mathematics; Statistics","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":[0.0003340271,0.00005515329,0.000116744,0.00008169471,0.0001185501,0.000154045,0.000982844,0.00002509723,0.000005964703],"category_scores_gemma":[0.0001086868,0.00004689457,0.00001383542,0.00006045996,0.000006508161,0.0003272946,0.0003117046,0.0001209167,0.000004131143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001241993,"about_ca_system_score_gemma":0.00001461335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003957495,"about_ca_topic_score_gemma":0.0004066254,"domain_scores_codex":[0.9992577,0.000059085,0.0001318517,0.0002535688,0.0001641622,0.0001335838],"domain_scores_gemma":[0.9991436,0.00002935667,0.00004592801,0.0007016484,0.00002508892,0.00005437944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001220583,0.0003559418,0.8048872,0.000006607165,0.000008115561,0.00003184268,0.03942238,0.07636219,0.001633615,0.001689467,0.0001186125,0.07547181],"study_design_scores_gemma":[0.0005832904,0.0003453196,0.4690641,0.00001687708,0.000001586876,0.000002313777,0.001876633,0.5270151,0.0006039246,0.0002601347,0.00009762568,0.0001330892],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8347865,0.000003223814,0.1628194,0.0003217934,0.00007805878,0.00009679404,1.791588e-8,0.00003721397,0.001857005],"genre_scores_gemma":[0.9661432,1.939116e-7,0.03351095,0.00006259471,0.00002618181,0.000003494655,3.603528e-8,0.000002828237,0.0002505005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4506529,"threshold_uncertainty_score":0.1912305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04669500883657094,"score_gpt":0.3860673460005514,"score_spread":0.3393723371639805,"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."}}