{"id":"W4253142094","doi":"10.5539/cis.v12n2p155","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 12, No. 2","year":2019,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Big Data and Digital Economy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Library science; Information retrieval; Data science","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":["scholarly_communication","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001269503,0.0001601837,0.0001860418,0.0005837082,0.0004309456,0.002893199,0.001134476,0.00003878327,0.00001549201],"category_scores_gemma":[0.0004625636,0.0001352977,0.00003132534,0.0008585001,0.0004471131,0.09837668,0.001201347,0.00007184441,0.001170512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005203364,"about_ca_system_score_gemma":0.0002494687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001432646,"about_ca_topic_score_gemma":1.864856e-7,"domain_scores_codex":[0.9983627,0.000008039256,0.0004760773,0.0003189332,0.0004467211,0.0003875674],"domain_scores_gemma":[0.9933823,0.00005094181,0.0002044113,0.0004864335,0.005652603,0.0002232506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006115582,0.00002069196,0.001188838,0.0002403456,0.000004993082,5.076009e-8,0.001079297,0.00007601017,0.00001052663,0.04846554,0.0499775,0.8989301],"study_design_scores_gemma":[0.0004563367,0.0001347593,0.008833841,0.00004649093,0.000001506314,0.000002712654,0.000007660369,0.4358855,0.00006189292,0.0001760106,0.5542178,0.0001754773],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008456591,0.0000456232,0.9591561,0.0001092451,0.01166036,0.0008803259,0.00001889353,0.0001070269,0.01956582],"genre_scores_gemma":[0.2031842,0.0009113916,0.7510961,0.04089712,0.002760289,0.0001963636,0.0002350823,0.00002182252,0.0006975404],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8987546,"threshold_uncertainty_score":0.9996072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01695451060752471,"score_gpt":0.2453093130286776,"score_spread":0.2283548024211529,"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."}}