{"id":"W2798653217","doi":"10.1145/3209978.3210021","title":"Automated Comparative Table Generation for Facilitating Human Intervention in Multi-Entity Resolution","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto; National Natural Science Foundation of China","keywords":"Computer science; Automatic summarization; Crowdsourcing; Pairwise comparison; Table (database); Human-in-the-loop; Machine learning; Data mining; Graph; Set (abstract data type); Process (computing); Information retrieval; Artificial intelligence; Theoretical computer science; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005059764,0.00007332944,0.0001521164,0.0002049975,0.0002743353,0.00026428,0.0002452143,0.00003695308,0.0003220029],"category_scores_gemma":[0.0009666602,0.00006109893,0.00004861387,0.0004076931,0.00007625478,0.0008079602,0.0001204042,0.00003850121,0.0002301537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006947233,"about_ca_system_score_gemma":0.000009426861,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001034706,"about_ca_topic_score_gemma":0.02498412,"domain_scores_codex":[0.9981923,0.0003127345,0.0006177648,0.0003621569,0.0003527032,0.0001623122],"domain_scores_gemma":[0.9990402,0.0001741776,0.0001688999,0.0002669347,0.0003211025,0.00002861865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001910096,0.001888931,0.004531633,0.0000888025,0.00008109736,0.00000154597,0.01738598,0.003637751,0.06574914,0.1578913,0.7066289,0.0419239],"study_design_scores_gemma":[0.0009346518,0.0001711669,0.007621182,0.00001430459,0.000004081533,1.116872e-7,0.004412125,0.9686497,0.002672356,0.003180238,0.01224129,0.00009881325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3644726,0.000009557706,0.6327113,0.00009871675,0.0002429335,0.0004661201,0.00005045047,0.0001067802,0.001841527],"genre_scores_gemma":[0.9679509,2.988811e-7,0.02627371,0.00007976149,0.00004323126,0.00004946731,0.0001817322,0.000002378206,0.005418497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.965012,"threshold_uncertainty_score":0.9928074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5955459466693872,"score_gpt":0.5512337355109547,"score_spread":0.04431221115843254,"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."}}