{"id":"W2756914837","doi":"10.1145/3121050.3121052","title":"Mining the Temporal Statistics of Query Terms for Searching Social Media Posts","year":2017,"lang":"en","type":"article","venue":"","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Ranking (information retrieval); Timestamp; Relevance (law); Information retrieval; Query expansion; Kernel density estimation; Data mining; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0020848,0.0009737362,0.001109719,0.01026689,0.0004700753,0.001273106,0.001049839,0.0009350427,0.0004480069],"category_scores_gemma":[0.01371128,0.0003833747,0.0008501261,0.006611268,0.0004222355,0.002809725,0.0006015434,0.0007713664,0.0006435469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008282591,"about_ca_system_score_gemma":0.001172854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005683266,"about_ca_topic_score_gemma":0.01263152,"domain_scores_codex":[0.9987352,0.0003373931,0.0001478748,0.0002712266,0.0004056393,0.0001026557],"domain_scores_gemma":[0.9934986,0.003642522,0.001406876,0.0004721852,0.0007917307,0.0001881178],"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":[0.001527992,0.001309379,0.1880378,0.00175231,0.0006443366,0.001107454,0.001029496,0.09111241,0.1113123,0.008484492,0.01169826,0.5819837],"study_design_scores_gemma":[0.00007000793,0.0004044367,0.0556728,0.00004727893,0.0001801369,0.0006178493,0.0004389026,0.9128023,0.0169527,0.00934807,0.003383882,0.00008154495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6020635,0.004361035,0.3812614,0.001240495,0.00009220731,0.0004234906,0.005212338,0.002933109,0.002412363],"genre_scores_gemma":[0.9120589,0.000932003,0.08104928,0.00007095375,0.0002198985,0.0002023493,0.004575392,0.00009684475,0.0007943693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01026689,"threshold_uncertainty_score":0.01130038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06087900319667349,"score_gpt":0.3299588423608942,"score_spread":0.2690798391642207,"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."}}