{"id":"W4253782455","doi":"10.5539/cis.v10n1p89","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 10, No. 1","year":2017,"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; Data science; Engineering ethics; Engineering","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":[0.04517306,0.002796542,0.006828481,0.01182312,0.00569796,0.01051536,0.005864565,0.01640781,0.1164864],"category_scores_gemma":[0.4716613,0.001828144,0.004213052,0.00512898,0.00287318,0.007120984,0.004012398,0.01074516,0.0689295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006344799,"about_ca_system_score_gemma":0.01132707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003706475,"about_ca_topic_score_gemma":0.006020978,"domain_scores_codex":[0.9466842,0.00975912,0.01121735,0.004238625,0.02596785,0.002132811],"domain_scores_gemma":[0.1864114,0.03317538,0.01355943,0.00714333,0.7505813,0.009129069],"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":[0.00002775647,0.000004288207,0.00009182049,0.0004646542,0.00000858664,0.00005489528,0.0000334176,0.000009987662,0.00003392107,0.0001245855,0.994526,0.004620064],"study_design_scores_gemma":[0.0001685417,0.0000413878,0.0009595127,0.003138288,0.0000882795,0.0008699664,0.000303904,0.0002655881,0.0002503549,0.001438194,0.9923564,0.0001195484],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0001367278,0.003790051,0.001038338,0.1341348,0.8554097,0.0006154181,0.000787914,0.0003953892,0.003691711],"genre_scores_gemma":[0.005283337,0.01022035,0.003284865,0.1668835,0.7359365,0.003293666,0.00206806,0.001246359,0.0717833],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.1164864,"threshold_uncertainty_score":0.3896858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02875460058313218,"score_gpt":0.2761192819825631,"score_spread":0.247364681399431,"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."}}