{"id":"W2158915197","doi":"10.1145/1067268.1067274","title":"The TREC terabyte retrieval track","year":2005,"lang":"en","type":"article","venue":"ACM SIGIR Forum","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Terabyte; Computer science; Information retrieval; Search engine indexing; Track (disk drive); Context (archaeology); Automatic indexing","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01681475,0.002216415,0.003008947,0.009058461,0.004311936,0.009672847,0.004690668,0.005324492,0.1009963],"category_scores_gemma":[0.02354424,0.0009102672,0.001448811,0.007697326,0.001487853,0.007825801,0.003412915,0.005077151,0.1169559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008704338,"about_ca_system_score_gemma":0.01900739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1148522,"about_ca_topic_score_gemma":0.1510261,"domain_scores_codex":[0.9884042,0.002168,0.0005571157,0.001033501,0.006743583,0.001093595],"domain_scores_gemma":[0.967402,0.002437351,0.0008457716,0.002832867,0.02266418,0.003817814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003369081,0.00003690179,0.0000542567,0.00009557448,0.000008409194,0.00001258953,0.00001467198,0.00008035386,0.0003669111,0.0003590446,0.9882357,0.01070183],"study_design_scores_gemma":[0.0001875443,0.000160809,0.00275437,0.0002475442,0.00004604318,0.00009900594,0.0001639416,0.001648083,0.002418651,0.002069306,0.9901191,0.00008551793],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0110316,0.04976553,0.03357079,0.09073599,0.05757688,0.009442914,0.3891985,0.0310902,0.3275875],"genre_scores_gemma":[0.01789314,0.01102907,0.02847279,0.0135835,0.007287991,0.003773618,0.4936718,0.003962612,0.4203255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1148522,"threshold_uncertainty_score":0.3378663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01575666061787809,"score_gpt":0.2564480901007232,"score_spread":0.2406914294828451,"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."}}