{"id":"W2740256306","doi":"10.1145/3077136.3080657","title":"Learning To Rank Resources","year":2017,"lang":"en","type":"article","venue":"","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Ranking (information retrieval); Computer science; Learning to rank; Selection (genetic algorithm); Rank (graph theory); Resource (disambiguation); Machine learning; Recall; Information retrieval; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002986468,0.001541659,0.001698697,0.004943614,0.001139622,0.002376928,0.002226454,0.001735793,0.01093509],"category_scores_gemma":[0.02138088,0.0004368367,0.0009207039,0.004252216,0.000967379,0.004500127,0.002306829,0.001805575,0.005704898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001159758,"about_ca_system_score_gemma":0.002119172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005286387,"about_ca_topic_score_gemma":0.008856254,"domain_scores_codex":[0.9956177,0.001499113,0.0002598672,0.0008592416,0.001339255,0.0004247632],"domain_scores_gemma":[0.9912155,0.004393002,0.0006370815,0.001756152,0.0015899,0.0004083323],"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":[0.0002743567,0.0003669385,0.00512125,0.0003296073,0.0001418227,0.0001347028,0.0001958686,0.0861629,0.003764119,0.02924224,0.04101337,0.8332528],"study_design_scores_gemma":[0.00004946754,0.0002765906,0.001363744,0.00007110572,0.0000603741,0.0002405189,0.0001293104,0.9034681,0.00558251,0.07473505,0.01395418,0.00006896214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01617637,0.0009045163,0.9723851,0.0005828409,0.000141155,0.0002237039,0.0008392541,0.00269337,0.006053689],"genre_scores_gemma":[0.3928083,0.0006998302,0.5886807,0.0004241286,0.0004112554,0.0004826978,0.003268078,0.0004192044,0.01280583],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01093509,"threshold_uncertainty_score":0.03658158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02713053001410242,"score_gpt":0.2966494220259451,"score_spread":0.2695188920118426,"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."}}