{"id":"W4231970171","doi":"10.5539/cis.v9n1p147","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 9, No. 1","year":2016,"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; Information retrieval; Data science; Library 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02344487,0.002055411,0.004583224,0.009799947,0.004427085,0.008315016,0.004484485,0.01014563,0.1877116],"category_scores_gemma":[0.3171416,0.001170756,0.002828161,0.004020213,0.002219583,0.005673455,0.003052562,0.007322775,0.1179312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004537025,"about_ca_system_score_gemma":0.008040513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003238924,"about_ca_topic_score_gemma":0.005556779,"domain_scores_codex":[0.9705098,0.004297875,0.004954752,0.002350174,0.01666955,0.001217917],"domain_scores_gemma":[0.2998899,0.02072262,0.008722836,0.006475504,0.6537718,0.01041729],"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":[0.00001374142,0.000002440983,0.00004428954,0.0001626098,0.000003403167,0.00003395693,0.00001753038,0.000006217256,0.0000262878,0.000110683,0.9954928,0.004085953],"study_design_scores_gemma":[0.00007492591,0.00002637841,0.0006063378,0.00111887,0.00003577791,0.0004882848,0.0001998834,0.0002034366,0.0002330073,0.001117131,0.9958262,0.00006970846],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0001330663,0.002505172,0.001213611,0.1197323,0.8675059,0.0004859792,0.000824084,0.000591427,0.007008476],"genre_scores_gemma":[0.005964331,0.007430408,0.003465468,0.1318672,0.64597,0.002368368,0.002439189,0.002013861,0.1984811],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.1877116,"threshold_uncertainty_score":0.6279579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02005128127764308,"score_gpt":0.2513886848851868,"score_spread":0.2313374036075437,"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."}}