{"id":"W3197731842","doi":"10.3390/app11178275","title":"Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Data science; Preprocessor; Variety (cybernetics); Field (mathematics); Information retrieval; Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007922152,0.0001294564,0.0002350401,0.0000696206,0.0003129993,0.0008852383,0.001127596,0.00003627933,0.0001292775],"category_scores_gemma":[0.0004284703,0.00009619418,0.00002665964,0.001383228,0.00008288493,0.001370621,0.000511905,0.00004839305,0.0001704642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005836673,"about_ca_system_score_gemma":0.00003870099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003192471,"about_ca_topic_score_gemma":0.00002393546,"domain_scores_codex":[0.9986545,0.000007211179,0.0002914506,0.0005462216,0.0002947062,0.0002059063],"domain_scores_gemma":[0.9988101,0.00006515092,0.0001902786,0.0007714066,0.0001540209,0.000009031329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001784325,0.0002151545,0.000131461,0.461992,0.00007244291,0.00005297639,0.00003646299,0.0001029811,0.002324103,0.1239894,0.4010029,0.01006224],"study_design_scores_gemma":[0.0004348897,0.00001297365,0.00003514023,0.08551184,0.0007305398,0.0001339702,0.0002643577,0.03245964,0.0021746,0.007312203,0.8697591,0.001170716],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.001803073,0.7254992,0.2023472,0.01971253,0.004998394,0.01109748,0.00778683,0.000822411,0.02593286],"genre_scores_gemma":[0.5581378,0.07737186,0.04011897,0.1628404,0.008254036,0.002032801,0.1495214,0.0002130311,0.001509794],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.6481274,"threshold_uncertainty_score":0.8536373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1250664883917893,"score_gpt":0.3314910018240527,"score_spread":0.2064245134322635,"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."}}