{"id":"W4226187801","doi":"10.5539/cis.v15n2p89","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 15, No. 2","year":2022,"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001715278,0.0001467854,0.0001632451,0.0006818328,0.001372188,0.002376163,0.001369701,0.00002047833,0.00002577248],"category_scores_gemma":[0.0004887155,0.000134932,0.00003146489,0.001399759,0.0004924412,0.07156961,0.002601516,0.00009957069,0.0002190002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008813687,"about_ca_system_score_gemma":0.0003135506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001688708,"about_ca_topic_score_gemma":1.432211e-7,"domain_scores_codex":[0.9982359,0.00001465676,0.0004793375,0.0003093419,0.0005814345,0.0003793273],"domain_scores_gemma":[0.9945002,0.00004830763,0.0002174679,0.0004270082,0.004595166,0.0002118048],"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":[0.000006296152,0.00002764105,0.0002718591,0.0001125487,0.000004345996,1.132744e-7,0.001505694,0.0002429865,0.000004739774,0.04449384,0.1179168,0.8354132],"study_design_scores_gemma":[0.0003297224,0.0001388687,0.002597095,0.0000118076,0.00000145193,0.000004907761,0.00001582305,0.390982,0.00002442013,0.000209572,0.6055408,0.000143539],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003190535,0.0000726286,0.9768687,0.0001823588,0.01089248,0.0006849478,0.00004570357,0.0001097562,0.00795283],"genre_scores_gemma":[0.1999719,0.0007232201,0.7257662,0.0686816,0.003115632,0.0007772867,0.0004586564,0.00002565421,0.0004798251],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8352696,"threshold_uncertainty_score":0.9999279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01946651010862913,"score_gpt":0.2495630443819015,"score_spread":0.2300965342732723,"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."}}