{"id":"W4254940179","doi":"10.5539/cis.v11n2p108","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 11, No. 2","year":2018,"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":[0.03851014,0.002944529,0.006357742,0.01126926,0.005261833,0.01039946,0.005307425,0.01507974,0.1290325],"category_scores_gemma":[0.4124821,0.001770473,0.003990693,0.004727642,0.002662953,0.006981529,0.00379875,0.009511868,0.0739933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00521915,"about_ca_system_score_gemma":0.009525122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003180731,"about_ca_topic_score_gemma":0.004911421,"domain_scores_codex":[0.9521062,0.008270384,0.009452168,0.003635395,0.02460218,0.001933773],"domain_scores_gemma":[0.2147096,0.02804703,0.01137071,0.007138853,0.7295544,0.009179425],"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":[0.00002836209,0.00000411069,0.00008022436,0.0003799437,0.000008313103,0.00006361747,0.00002992327,0.000009649992,0.00003913192,0.0001256019,0.9947438,0.004487271],"study_design_scores_gemma":[0.0001710323,0.00004320644,0.0008803344,0.0024556,0.00008012739,0.0009976185,0.0002822916,0.0002879384,0.0002829153,0.00134498,0.993061,0.0001129891],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0001395804,0.003305845,0.001155623,0.1074082,0.8819427,0.000574469,0.0006669075,0.0004119711,0.004394692],"genre_scores_gemma":[0.005306819,0.008441235,0.003045661,0.1392822,0.746767,0.002827337,0.001789973,0.001272183,0.09126766],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.1290325,"threshold_uncertainty_score":0.4316568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0212166855610326,"score_gpt":0.2621747790639105,"score_spread":0.2409580935028779,"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."}}