{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000218399,0.00003364622,0.00004040727,0.0000407321,0.0005272182,0.000667462,0.0008574015,0.00001473334,0.00006865529],"category_scores_gemma":[0.0001124093,0.0000252314,0.00001960885,0.00004276578,0.00001384884,0.0005878822,0.0003218928,0.00006559968,0.0009120661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006457632,"about_ca_system_score_gemma":0.00001037968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004034154,"about_ca_topic_score_gemma":0.000002720867,"domain_scores_codex":[0.9995395,0.00001078817,0.00006768848,0.00007936786,0.0001770845,0.0001255936],"domain_scores_gemma":[0.9994787,0.00001308229,0.00002913683,0.0003498884,0.00004999849,0.00007919355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001629012,0.00002693223,0.01227534,0.000009028441,0.00000628154,0.00001903929,0.01150839,0.0001630887,0.001630759,0.0875685,0.00691992,0.8798564],"study_design_scores_gemma":[0.0004567078,0.0002695732,0.2319981,0.00001423035,0.000001671495,0.000008646049,0.0002645011,0.03047775,0.008211584,0.0004169962,0.7275963,0.0002839458],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.588031,0.000002707456,0.2046575,0.006314158,0.0002181287,0.0001235341,2.53968e-7,0.0002285156,0.2004242],"genre_scores_gemma":[0.9668356,8.80782e-7,0.01106534,0.0004131857,0.00002683629,0.000003001372,1.572171e-7,0.000001370037,0.02165361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8795725,"threshold_uncertainty_score":0.9998658,"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."}}