{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02957073,0.00247669,0.005197313,0.01008895,0.005147429,0.01005347,0.004883878,0.01454206,0.1643452],"category_scores_gemma":[0.3190995,0.001513602,0.003425888,0.004306674,0.002162649,0.005969396,0.003307043,0.008545267,0.1048232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00474393,"about_ca_system_score_gemma":0.009835803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003390143,"about_ca_topic_score_gemma":0.005764038,"domain_scores_codex":[0.9645238,0.005769738,0.006128944,0.002615918,0.01936563,0.001595964],"domain_scores_gemma":[0.297228,0.02418309,0.009485165,0.006307407,0.6521043,0.01069198],"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":[0.00001898285,0.000003199855,0.00005236892,0.0001952664,0.000004607114,0.00004072044,0.00001463263,0.000006531259,0.00002623644,0.00008812366,0.9960365,0.003512876],"study_design_scores_gemma":[0.0001297889,0.00003707955,0.0006836471,0.001467526,0.00004944457,0.0006539633,0.0001765808,0.0002047357,0.0002037509,0.0009574856,0.9953561,0.000079763],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0001293201,0.002735038,0.001038649,0.1112521,0.8767205,0.0005320353,0.0007415768,0.0004658006,0.006384911],"genre_scores_gemma":[0.004584985,0.008228132,0.002792601,0.1434235,0.6941534,0.002516043,0.002345558,0.001421345,0.1405345],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.1643452,"threshold_uncertainty_score":0.5497895,"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."}}