{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.02814444,0.002250929,0.004766069,0.009064045,0.00492946,0.009433041,0.004854647,0.01435764,0.1678151],"category_scores_gemma":[0.2988145,0.001396287,0.003484651,0.004032054,0.002108145,0.005421881,0.003130846,0.008265709,0.1053019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005041643,"about_ca_system_score_gemma":0.01009925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003763218,"about_ca_topic_score_gemma":0.006282964,"domain_scores_codex":[0.9685686,0.005063065,0.005477659,0.002152319,0.01723955,0.001498787],"domain_scores_gemma":[0.3140807,0.02166281,0.009110159,0.005951711,0.6385503,0.01064425],"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":[0.00001961988,0.000003066158,0.0000588759,0.0001890853,0.00000458712,0.00004001658,0.00001327515,0.000007123994,0.00002618664,0.00009536627,0.9957693,0.003773461],"study_design_scores_gemma":[0.0001264443,0.00003462902,0.0006755407,0.001374745,0.00005128738,0.000614542,0.0001618469,0.000207123,0.0002017803,0.001040423,0.9954359,0.00007568528],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0001450576,0.002813165,0.001112147,0.1227925,0.8643304,0.0005595147,0.0007515879,0.0004634262,0.007032204],"genre_scores_gemma":[0.005262985,0.009142248,0.003151427,0.1496576,0.6635476,0.002572997,0.002374446,0.001399229,0.1628915],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8321849,"threshold_uncertainty_score":0.5613973,"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."}}