{"id":"W4255826610","doi":"10.5539/cis.v14n1p54","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 14, No. 1","year":2021,"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; Library 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":[],"consensus_categories":[],"category_scores_codex":[0.03340153,0.002320717,0.005327134,0.0105674,0.005453207,0.01021954,0.005060011,0.01486457,0.1595259],"category_scores_gemma":[0.3568842,0.001519252,0.003415097,0.004624992,0.002218827,0.006097245,0.003427826,0.009149819,0.1046455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005466326,"about_ca_system_score_gemma":0.01073169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003593725,"about_ca_topic_score_gemma":0.0061923,"domain_scores_codex":[0.960732,0.006816918,0.006834214,0.003034249,0.02085109,0.001731397],"domain_scores_gemma":[0.2619984,0.02799569,0.01031565,0.006480473,0.6827666,0.01044314],"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":[0.00001798667,0.000003186575,0.00005470504,0.0002179365,0.000004298721,0.00003119979,0.00001538647,0.00000690325,0.0000226722,0.00009227201,0.9958526,0.003680759],"study_design_scores_gemma":[0.0001083295,0.00003456585,0.0006774394,0.001693776,0.00004713017,0.0005172432,0.0001671601,0.0001828509,0.0001770257,0.0009551091,0.9953615,0.00007791429],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0001278643,0.003226357,0.001040589,0.1362948,0.8510731,0.0005512048,0.0008670932,0.0004649478,0.00635407],"genre_scores_gemma":[0.004567428,0.01006628,0.003013538,0.1628752,0.6826448,0.002742203,0.002722823,0.001450246,0.1299175],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.1595259,"threshold_uncertainty_score":0.5336673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0214864686251845,"score_gpt":0.256499374474908,"score_spread":0.2350129058497235,"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."}}