{"id":"W3033527224","doi":"10.1145/3385188","title":"Asterisk","year":2020,"lang":"en","type":"article","venue":"ACM/IMS Transactions on Data Science","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); University of Alberta","funders":"","keywords":"Computer science; Heuristics; Quality (philosophy); Machine learning; Preprocessor; Process (computing); Artificial intelligence; Annotation; IBM; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0004896931,0.0001306336,0.0001162039,0.0001145337,0.0005602127,0.0004334104,0.008285082,0.00002769839,0.00009263845],"category_scores_gemma":[0.0002017373,0.0001148658,0.00003106113,0.001702139,0.0002317273,0.002588109,0.0002571104,0.0003283363,0.0004901678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001851924,"about_ca_system_score_gemma":0.0001666952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003478943,"about_ca_topic_score_gemma":0.000004819406,"domain_scores_codex":[0.9979047,0.00004174601,0.0001693804,0.0009480175,0.0005935844,0.0003426038],"domain_scores_gemma":[0.9969416,0.00008832551,0.00005133266,0.002580889,0.000049358,0.0002884732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009362915,0.0001200902,0.00007081567,0.00001215999,0.00001115951,0.00002162418,0.001212453,0.003694179,0.003615147,0.002057899,0.001456828,0.9877183],"study_design_scores_gemma":[0.0002859492,0.0002629427,0.0009038241,0.00001646556,0.000008369916,0.0000273987,0.00007487123,0.934579,0.003295711,0.0002829031,0.05994931,0.0003132114],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001566701,0.00001952536,0.9815032,0.01497802,0.0004497054,0.00008196936,0.00006852633,0.0003166833,0.001015709],"genre_scores_gemma":[0.8208535,0.00002922191,0.1761276,0.002729372,0.00008825705,0.000005708147,0.000008238918,0.000008787587,0.0001492462],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9874051,"threshold_uncertainty_score":0.9970806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.076600776069982,"score_gpt":0.3117897892712219,"score_spread":0.2351890132012399,"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."}}