{"id":"W2899154813","doi":"10.1145/3239571","title":"Anserini","year":2018,"lang":"en","type":"article","venue":"Journal of Data and Information Quality","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":230,"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; Information retrieval; World Wide Web; Ranking (information retrieval); Context (archaeology); Search engine indexing; Implementation; Data science; Software engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001799534,0.00004056701,0.00008620435,0.00009874949,0.00008705493,0.0002323006,0.0007086478,0.0000251479,0.00002811188],"category_scores_gemma":[0.0001646532,0.00002917936,0.00001643538,0.0001605151,0.00004830942,0.02517032,0.0002641701,0.00008664531,0.00005377135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009352794,"about_ca_system_score_gemma":0.00008027624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004730763,"about_ca_topic_score_gemma":5.43782e-7,"domain_scores_codex":[0.9989668,0.00003946371,0.000561218,0.00003411448,0.0003187978,0.00007960243],"domain_scores_gemma":[0.9987403,0.00003476776,0.0003727859,0.000323453,0.000449405,0.00007929713],"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":[0.00006991973,0.00003736054,0.00224117,0.00005612273,0.00001982484,0.000001242331,0.005707963,7.286289e-7,0.0001231654,0.1165566,0.01632141,0.8588645],"study_design_scores_gemma":[0.000881172,0.0004357025,0.09708066,0.0000214165,0.000006304466,0.0001525079,0.0005163454,0.01420645,0.000936305,0.0009835155,0.8846242,0.0001553723],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1500605,0.00002018952,0.8429226,0.001937586,0.0004073869,0.00006370513,0.00005605318,0.00002186618,0.004510132],"genre_scores_gemma":[0.9488256,0.0001039695,0.04834303,0.00246861,0.0001870983,3.315828e-7,0.00003551021,0.000001279739,0.00003455031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8683028,"threshold_uncertainty_score":0.9884641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1067568912519719,"score_gpt":0.3914247076637422,"score_spread":0.2846678164117703,"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."}}