{"id":"W4389153826","doi":"10.5539/cis.v16n4p84","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 16, No. 4","year":2023,"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":["scholarly_communication","insufficient_payload"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001639526,0.0001545848,0.0001688462,0.0008937048,0.0007002421,0.003000972,0.001110964,0.00003698852,0.000007106982],"category_scores_gemma":[0.0008705623,0.0001328861,0.00003102705,0.002155413,0.0005739419,0.08421273,0.001442643,0.00006686745,0.001193672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006046676,"about_ca_system_score_gemma":0.0002409907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001610316,"about_ca_topic_score_gemma":2.610821e-7,"domain_scores_codex":[0.9983062,0.00000848701,0.0004785395,0.0003065365,0.0004630455,0.00043715],"domain_scores_gemma":[0.9931454,0.00006342507,0.0001756288,0.000424263,0.005949751,0.0002415209],"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":[0.000002794069,0.000009599929,0.0002391367,0.0001353102,0.000003201785,7.620881e-8,0.0007957406,0.00005429428,0.000003630266,0.02966393,0.1353449,0.8337474],"study_design_scores_gemma":[0.0003297177,0.00008572326,0.006909329,0.00003802872,0.00000147005,0.000002158051,0.00001085009,0.4488654,0.00005385024,0.0003626532,0.5431838,0.0001570469],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003850453,0.00003236708,0.9760844,0.0001899829,0.009544053,0.0006015701,0.00002551609,0.0002310961,0.009440521],"genre_scores_gemma":[0.218682,0.003215307,0.7092599,0.05984918,0.006427255,0.000602511,0.0008542229,0.00004272488,0.001066843],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8335903,"threshold_uncertainty_score":0.999584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02612382323586549,"score_gpt":0.2665357055463607,"score_spread":0.2404118823104953,"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."}}