{"id":"W3008513288","doi":"10.1109/ms.2020.2975159","title":"An Exploratory Study of Machine Learning Model Stores","year":2020,"lang":"en","type":"article","venue":"IEEE Software","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; York University","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données","keywords":"Computer science; Machine learning; Artificial intelligence; Knowledge management; Software engineering; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00719711,0.0003449701,0.0005545727,0.00193167,0.002139019,0.004199557,0.002614139,0.00186272,0.007264462],"category_scores_gemma":[0.06633497,0.000672153,0.0005108984,0.004143522,0.001665408,0.008568633,0.002185024,0.002761966,0.0009636189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001715787,"about_ca_system_score_gemma":0.00161276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009567873,"about_ca_topic_score_gemma":0.01321914,"domain_scores_codex":[0.9957944,0.002006424,0.000308381,0.0004831651,0.00108777,0.000319815],"domain_scores_gemma":[0.9106798,0.07038618,0.00467855,0.006806107,0.00605335,0.001396048],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004931633,0.01644926,0.5384671,0.001729489,0.0004571922,0.004575697,0.06169769,0.03976717,0.007477044,0.09218307,0.02804836,0.2042163],"study_design_scores_gemma":[0.0008424103,0.009109474,0.2529798,0.001380272,0.000401026,0.004099906,0.1693255,0.3782305,0.01982685,0.03991632,0.1234535,0.0004345074],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888633,0.0002157767,0.002415051,0.000970695,0.00001263347,0.0001346665,0.0007425526,0.0001563187,0.00648899],"genre_scores_gemma":[0.9889514,0.0002011006,0.006040324,0.0001668078,0.00001721963,0.0001010681,0.001578488,0.0001067813,0.002836897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9928029,"threshold_uncertainty_score":0.03806239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101877395413137,"score_gpt":0.2916997906936815,"score_spread":0.1898223952805445,"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."}}