{"id":"W3111762657","doi":"10.23889/ijpds.v5i5.1621","title":"MASK: A Success Story for An International Collaboration","year":2020,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Identification (biology); Masking (illustration); Process (computing); Protected health information; Software; Interface (matter); Artificial intelligence; Machine learning; Information retrieval; Data science; World Wide Web; Public health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0148359,0.0009590344,0.0006333445,0.0012701,0.01365741,0.0169483,0.00186888,0.008793519,0.03828776],"category_scores_gemma":[0.02153662,0.000417172,0.0006704294,0.001789141,0.00669947,0.01773576,0.01822562,0.011637,0.01457186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004518462,"about_ca_system_score_gemma":0.008313596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005372543,"about_ca_topic_score_gemma":0.005851439,"domain_scores_codex":[0.9873707,0.005307384,0.0004626541,0.001251386,0.002902238,0.002705524],"domain_scores_gemma":[0.9817892,0.003000704,0.0008861831,0.001404196,0.0027805,0.01013919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001371831,0.00007531215,0.001837877,0.0001736489,0.00002387892,0.00166523,0.01247922,0.0001512657,0.0003931646,0.1339758,0.8009641,0.04812337],"study_design_scores_gemma":[0.00001346448,0.00003519865,0.0006788779,0.0001772567,0.00000461198,0.0004804256,0.006856411,0.0001173196,0.000120367,0.007586989,0.9839038,0.00002527941],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01660041,0.009218624,0.002451035,0.8142506,0.01651179,0.00006973918,0.0007368841,0.0006835793,0.1394775],"genre_scores_gemma":[0.3758146,0.009458249,0.006517857,0.2803445,0.01038339,0.0003620508,0.001981964,0.001940087,0.3131972],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03828776,"threshold_uncertainty_score":0.1280853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09876678348695475,"score_gpt":0.4311233464971597,"score_spread":0.332356563010205,"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."}}