{"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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001346405,0.0001595349,0.0001660788,0.0006033815,0.00080948,0.003122425,0.001168086,0.00003804281,0.00001177869],"category_scores_gemma":[0.000765978,0.0001344254,0.00002724265,0.001187329,0.001172832,0.09253466,0.00126485,0.00005352109,0.0006638244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004897586,"about_ca_system_score_gemma":0.0002588195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002028788,"about_ca_topic_score_gemma":6.311905e-7,"domain_scores_codex":[0.9983652,0.000008487413,0.0004826908,0.0003133614,0.0004245394,0.0004056822],"domain_scores_gemma":[0.9889055,0.00004251732,0.0002008105,0.0004576621,0.01014226,0.0002512812],"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":[0.000004707271,0.00001858918,0.0004702521,0.0001188508,0.000003677669,5.067859e-8,0.001335222,0.000009619979,0.000007616944,0.04919384,0.1032296,0.845608],"study_design_scores_gemma":[0.0003425355,0.0001553013,0.005137181,0.00003863123,0.000001692987,0.000003156737,0.000006592006,0.3651203,0.0001514647,0.0002284015,0.628652,0.0001627614],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004263816,0.00002639654,0.9765359,0.0001206558,0.009189306,0.0004810769,0.00001561152,0.00009554115,0.009271722],"genre_scores_gemma":[0.1386827,0.0004329113,0.8223021,0.0329855,0.005095624,0.0001388391,0.0001198632,0.00001440919,0.0002280056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8454452,"threshold_uncertainty_score":0.9979124,"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."}}