{"id":"W3196524514","doi":"10.1145/3439333","title":"A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Interactive Intelligent Systems","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; Deutscher Akademischer Austauschdienst","keywords":"Computer science; Visual analytics; Machine learning; Summative assessment; Artificial intelligence; Process (computing); Set (abstract data type); Analytics; Taxonomy (biology); Property (philosophy); Formative assessment; Visualization; Data mining; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0003250377,0.0002091384,0.0003287981,0.000309541,0.0002010928,0.000271033,0.001088258,0.00005688245,0.00003869341],"category_scores_gemma":[0.0002702305,0.0001724206,0.0000573728,0.0006193828,0.00003148319,0.0006450045,0.0002094253,0.0002754115,0.00004354609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001098076,"about_ca_system_score_gemma":0.00008510941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002621434,"about_ca_topic_score_gemma":0.0001421903,"domain_scores_codex":[0.9979402,0.0002943449,0.0004806241,0.0007367168,0.0003275241,0.0002206023],"domain_scores_gemma":[0.9979749,0.0002279618,0.0001591211,0.00112536,0.0003315996,0.0001810733],"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":[0.0006693598,0.005079166,0.001665428,0.0009465971,0.00363815,0.0000967446,0.03562581,0.0738167,0.05280298,0.01999574,0.002839252,0.8028241],"study_design_scores_gemma":[0.000971997,0.00119281,0.0001769579,0.002640085,0.000192628,0.0001258868,0.03320968,0.1867517,0.3412478,0.0001445887,0.4320082,0.001337667],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002220996,0.00004684991,0.9953524,0.0006225402,0.0003514297,0.0005514564,0.00009585725,0.00006963847,0.0006888035],"genre_scores_gemma":[0.9871392,0.00003166838,0.01065709,0.0001350836,0.00003472473,0.00009837966,0.00005642354,0.00002022606,0.001827215],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9849182,"threshold_uncertainty_score":0.7031108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3421740435081893,"score_gpt":0.3559695203939969,"score_spread":0.01379547688580768,"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."}}