{"id":"W2894874337","doi":"10.1145/3209280.3232788","title":"The Quest for Total Recall","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Due diligence; Recall; Computer science; Categorization; Precision and recall; Haystack; Information retrieval; Government (linguistics); Enforcement; Data science; Knowledge management; Business; Psychology; Political science; World Wide Web; Finance; Artificial intelligence","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":[0.1137096,0.002763286,0.005903215,0.01445329,0.002651894,0.01556647,0.00587963,0.005396885,0.007243667],"category_scores_gemma":[0.3896022,0.001924452,0.003058506,0.01244942,0.009415604,0.03133532,0.007189277,0.006739925,0.004825179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005493618,"about_ca_system_score_gemma":0.00572455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004963286,"about_ca_topic_score_gemma":0.003782186,"domain_scores_codex":[0.8718408,0.062884,0.0141694,0.02385454,0.02569903,0.001552229],"domain_scores_gemma":[0.6161007,0.2983657,0.0117239,0.04459889,0.02769477,0.00151607],"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":[0.0007982983,0.0001642628,0.02014082,0.007309707,0.003303614,0.0002120212,0.002350834,0.006725792,0.001102597,0.3687179,0.06411133,0.5250627],"study_design_scores_gemma":[0.0001157097,0.0003015515,0.005455529,0.001824784,0.0006254779,0.0006067644,0.0007738282,0.009637804,0.001127823,0.9082648,0.07109377,0.0001721655],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.02810395,0.1850534,0.5779976,0.08017161,0.005841923,0.001004947,0.009215909,0.002284076,0.1103265],"genre_scores_gemma":[0.4681794,0.05124619,0.4079363,0.03251768,0.01131709,0.003149786,0.009101033,0.001521322,0.01503129],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1137096,"threshold_uncertainty_score":0.6013613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.316517329864422,"score_gpt":0.5082093572738737,"score_spread":0.1916920274094517,"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."}}