{"id":"W4403871802","doi":"10.5539/cis.v17n2p60","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 17, No. 2","year":2024,"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; Data 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":[0.00136393,0.0001602464,0.0001557397,0.0007610686,0.0005116279,0.005901764,0.0009668762,0.00003707441,0.00001116662],"category_scores_gemma":[0.0004807501,0.0001317202,0.00003455603,0.001404657,0.0005785433,0.1064633,0.001062685,0.00008277177,0.0006271991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005972965,"about_ca_system_score_gemma":0.0003289687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001138258,"about_ca_topic_score_gemma":1.69053e-7,"domain_scores_codex":[0.9983996,0.000007007279,0.0004701098,0.0003348074,0.0004216328,0.0003668298],"domain_scores_gemma":[0.9944912,0.00006295559,0.0001021393,0.0003702329,0.004748011,0.0002254411],"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":[0.000001754568,0.000007772966,0.00004767864,0.0002121491,0.000003807182,1.193068e-7,0.0008061804,0.00003022241,0.000003289373,0.05179918,0.1222533,0.8248345],"study_design_scores_gemma":[0.0001405799,0.00006781867,0.001186984,0.00006792509,0.000001763509,0.000003774229,0.000004338196,0.4548043,0.00003447969,0.0002643123,0.5433033,0.0001204155],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009061985,0.0001744803,0.9758452,0.0001757086,0.01092796,0.0004611867,0.00002355421,0.0001644999,0.01132122],"genre_scores_gemma":[0.09635767,0.002136729,0.8562063,0.03828038,0.005531698,0.0003146413,0.0002997225,0.00002791671,0.000844957],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8247141,"threshold_uncertainty_score":0.9951302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01980912800686967,"score_gpt":0.2631984552517191,"score_spread":0.2433893272448494,"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."}}