{"id":"W4307866166","doi":"10.5539/cis.v15n4p80","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 15, No. 4","year":2022,"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.001723302,0.0001468144,0.0001632578,0.0006822638,0.001372565,0.002377001,0.001370432,0.00002047823,0.00002529768],"category_scores_gemma":[0.0004877908,0.0001349603,0.00003147012,0.001400066,0.0004925467,0.07158234,0.002601092,0.0000995701,0.0002191484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008812348,"about_ca_system_score_gemma":0.0003138579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000167837,"about_ca_topic_score_gemma":1.438049e-7,"domain_scores_codex":[0.9982353,0.00001464776,0.0004794766,0.0003094437,0.0005816169,0.0003794505],"domain_scores_gemma":[0.9945285,0.00004858843,0.0002172844,0.0004273361,0.004566444,0.0002118267],"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":[0.000006467796,0.00002790671,0.0002823978,0.0001136067,0.000004383603,1.132905e-7,0.001520011,0.0002416061,0.000004869707,0.04534995,0.1157326,0.8367161],"study_design_scores_gemma":[0.0003316134,0.0001410683,0.002636965,0.00001196751,0.00000145915,0.000004918998,0.00001598399,0.3944181,0.00002463647,0.0002111437,0.6020579,0.0001442915],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003188553,0.00007163713,0.9771976,0.0001806668,0.0107584,0.0006808036,0.00004523237,0.0001088898,0.007768175],"genre_scores_gemma":[0.2054494,0.0007060246,0.7214109,0.06769455,0.003037581,0.0007662324,0.0004444133,0.00002530214,0.0004655698],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8365718,"threshold_uncertainty_score":0.9999275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01998456725953239,"score_gpt":0.2501311193730197,"score_spread":0.2301465521134873,"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."}}