{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004391655,0.001382338,0.001320268,0.004665756,0.002545107,0.006867705,0.002537536,0.001988963,0.1391873],"category_scores_gemma":[0.01659606,0.000774427,0.001161966,0.003775898,0.001315966,0.007519593,0.005211914,0.002431556,0.1528894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002295859,"about_ca_system_score_gemma":0.003203284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004579382,"about_ca_topic_score_gemma":0.005225856,"domain_scores_codex":[0.9941661,0.001074485,0.0003905293,0.001403015,0.002542177,0.0004236937],"domain_scores_gemma":[0.9922965,0.002050696,0.0003985959,0.002452871,0.002163561,0.0006376668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003865675,0.0001824367,0.002591538,0.0006043327,0.0000581545,0.0002520788,0.0004523397,0.002013202,0.004741944,0.05996548,0.3109477,0.6178042],"study_design_scores_gemma":[0.00003409402,0.0001094384,0.001257177,0.0001531599,0.00002788227,0.0005490099,0.00009432207,0.008790037,0.005517837,0.01858827,0.9648036,0.00007508496],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.01584384,0.01171018,0.2932384,0.01258994,0.005840839,0.0009887861,0.0129375,0.06034621,0.5865043],"genre_scores_gemma":[0.1044367,0.007031924,0.2440297,0.005872109,0.002175173,0.001032389,0.0305411,0.008911609,0.5959693],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1391873,"threshold_uncertainty_score":0,"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."}}