{"id":"W3177795256","doi":"10.52547/jipm.36.4.1081","title":"Extraction of Effective Textual and Semantic Features in Learning to Rank for Web Document Retrieval","year":2021,"lang":"en","type":"article","venue":"Iranian Journal of Information Processing and Management","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Toronto Metropolitan University","funders":"","keywords":"Information retrieval; Computer science; Rank (graph theory); Learning to rank; Document clustering; Document retrieval; World Wide Web; Semantic Web; Natural language processing; Artificial intelligence; Ranking (information retrieval); 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":[0.00174322,0.001169852,0.00145035,0.006909726,0.0007463909,0.001767032,0.0009525238,0.001433389,0.00159654],"category_scores_gemma":[0.0103686,0.0002503447,0.001175241,0.004715323,0.0006557595,0.003532689,0.0008902382,0.001119588,0.001765039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009055263,"about_ca_system_score_gemma":0.001393043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004968472,"about_ca_topic_score_gemma":0.006449099,"domain_scores_codex":[0.9975893,0.0006582601,0.0003163934,0.0003609767,0.0008196313,0.0002554045],"domain_scores_gemma":[0.9958708,0.00184038,0.0003958892,0.0004738804,0.001272195,0.000146884],"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":[0.0007875791,0.0007783089,0.0102653,0.0006782985,0.0001193339,0.0002188129,0.0001734877,0.02435744,0.02191423,0.005179177,0.01380224,0.9217258],"study_design_scores_gemma":[0.0002470347,0.0009354867,0.01523086,0.0001058025,0.0002660904,0.0005160763,0.000459875,0.9048887,0.04987034,0.0148786,0.01245196,0.0001490618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2955679,0.004062067,0.6714785,0.000898971,0.0003189394,0.001040338,0.006676343,0.01313808,0.006818907],"genre_scores_gemma":[0.5822353,0.0004930241,0.4056437,0.0001295565,0.0001669622,0.0003753816,0.008906174,0.0001865354,0.00186333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006909726,"threshold_uncertainty_score":0.009879112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007069426982035435,"score_gpt":0.273988742919462,"score_spread":0.2669193159374265,"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."}}