{"id":"W4409978955","doi":"10.5539/cis.v18n1p168","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 18, No. 1","year":2025,"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","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.03086932,0.002159683,0.004805231,0.01004687,0.005123686,0.009920763,0.004741674,0.01447254,0.1733886],"category_scores_gemma":[0.3334837,0.001408275,0.003299086,0.004289847,0.002011282,0.005936987,0.003230995,0.008536813,0.1091277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005283508,"about_ca_system_score_gemma":0.01051468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003767475,"about_ca_topic_score_gemma":0.006896189,"domain_scores_codex":[0.963461,0.006110607,0.006355698,0.002776682,0.01963077,0.001665303],"domain_scores_gemma":[0.2942684,0.02525783,0.0100838,0.006025524,0.6538113,0.01055317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001741984,0.000002943992,0.00005077189,0.0002041517,0.000003967707,0.00003188971,0.00001407034,0.000006135304,0.00002167112,0.00009170239,0.9958494,0.003705786],"study_design_scores_gemma":[0.0001007507,0.0000322851,0.0006544181,0.001603335,0.00004297874,0.000483417,0.000153866,0.0001619013,0.0001613129,0.00086457,0.9956719,0.00006926576],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0001226402,0.003196498,0.001012772,0.1275524,0.8592679,0.0005397509,0.0008954723,0.000453523,0.006959077],"genre_scores_gemma":[0.004712854,0.009586973,0.003058895,0.1652905,0.6635978,0.0025729,0.002821218,0.001373399,0.1469854],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8266114,"threshold_uncertainty_score":0.5800426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02035326639873538,"score_gpt":0.2700804940398904,"score_spread":0.249727227641155,"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."}}