{"id":"W2910420361","doi":"10.22230/src.2019v10n1a321","title":"Digital Science – Big Data and Diversified Risk","year":2019,"lang":"en","type":"article","venue":"Scholarly and Research Communication","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multinational corporation; Publishing; Scientific publishing; Big data; Digital library; Space (punctuation); Data science; World Wide Web; Computer science; Business; Political science; Public relations","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04900943,0.00005971512,0.00009412837,0.0006078177,0.001733951,0.0122239,0.005166384,0.00003146496,0.00002675066],"category_scores_gemma":[0.01800475,0.00004335218,0.0000110201,0.001823635,0.001152902,0.006394199,0.01548761,0.0005232227,0.0004014011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002480579,"about_ca_system_score_gemma":0.0001040911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000109868,"about_ca_topic_score_gemma":0.00003010916,"domain_scores_codex":[0.9956181,0.0005370751,0.0002324453,0.0008099708,0.002532722,0.0002696646],"domain_scores_gemma":[0.9915504,0.00201552,0.000090799,0.005296993,0.0008854868,0.0001607967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001657637,0.00003247913,0.09244433,0.00000305881,0.00000488464,4.332555e-7,0.0004917141,0.000002377104,0.0001985342,0.002136356,0.00523121,0.899438],"study_design_scores_gemma":[0.0006816457,0.0001164759,0.4877396,0.00005641383,0.00000647245,0.000005103509,0.009153746,0.03046537,0.00006373323,0.03876372,0.4327415,0.0002062135],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9672107,0.0009262146,0.0006589336,0.002133713,0.0002602944,0.0002631983,0.00008929348,0.00002530712,0.02843238],"genre_scores_gemma":[0.995744,0.000407574,0.0005914641,0.00002318541,0.00001968366,0.000001313364,0.00003227738,0.000002686619,0.003177777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8992319,"threshold_uncertainty_score":0.9995657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4635895852101309,"score_gpt":0.4798717061692719,"score_spread":0.016282120959141,"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."}}