{"id":"W4251259613","doi":"10.5539/cis.v13n2p87","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 13, No. 2","year":2020,"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; 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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.0008577634,0.0001587471,0.0001816407,0.0003565788,0.0005348743,0.002954644,0.001160702,0.00003421359,0.000008491605],"category_scores_gemma":[0.0009853388,0.0001365601,0.00003078569,0.001224654,0.0007117444,0.08817378,0.001300029,0.00007604142,0.0005497955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003503403,"about_ca_system_score_gemma":0.0002443936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001475355,"about_ca_topic_score_gemma":1.66153e-7,"domain_scores_codex":[0.9984044,0.000008274935,0.0004878822,0.0003200493,0.0004252258,0.000354126],"domain_scores_gemma":[0.9932032,0.00004185845,0.0001923209,0.0003207598,0.005879155,0.0003626806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006417708,0.00001370416,0.0002998938,0.0002143318,0.000004398035,9.333585e-8,0.001985278,0.00008050191,0.000006525188,0.03093899,0.1025183,0.8639316],"study_design_scores_gemma":[0.0003472582,0.0001276626,0.002552355,0.000024767,0.000001639406,0.00000159579,0.000009749029,0.5094228,0.00006362197,0.00009693289,0.487206,0.0001456622],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00157262,0.00005122279,0.9869813,0.0005144876,0.004389421,0.000501778,0.00001980553,0.0001067942,0.005862581],"genre_scores_gemma":[0.1435243,0.0009841437,0.7440434,0.1070046,0.003987265,0.0001589287,0.0001930048,0.00001777537,0.0000865072],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8637859,"threshold_uncertainty_score":0.9980804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02633485371931979,"score_gpt":0.2516663285142493,"score_spread":0.2253314747949295,"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."}}