{"id":"W4247081944","doi":"10.5539/cis.v8n3p292","title":"Reviewer Acknowledgements for Computer and Information Science, Vol. 8, No. 3","year":2015,"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","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":["scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.00176119,0.0001515928,0.0001681024,0.0005709114,0.0004371505,0.003024074,0.001081409,0.00003584446,0.000003793803],"category_scores_gemma":[0.001494534,0.0001275253,0.00002442153,0.001077945,0.0006135056,0.1026072,0.001225694,0.00006437297,0.0005152438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006828857,"about_ca_system_score_gemma":0.000412989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001860444,"about_ca_topic_score_gemma":2.088608e-7,"domain_scores_codex":[0.9983858,0.000009313943,0.0004632908,0.0002781806,0.0004969923,0.0003663735],"domain_scores_gemma":[0.9859952,0.00003750293,0.0001883786,0.0004120662,0.01298419,0.0003826702],"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":[0.000005592198,0.00001850469,0.0003843535,0.00009973397,0.000003488783,6.786325e-8,0.001511334,0.00006894421,0.000001872383,0.03293254,0.1599741,0.8049995],"study_design_scores_gemma":[0.0004713706,0.0001306695,0.002851256,0.00002949414,0.00000157005,0.000002889534,0.00001261393,0.4073132,0.00004443681,0.0003456868,0.5886416,0.000155266],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002160446,0.00005480465,0.9763185,0.0001097158,0.0104344,0.0004806311,0.00001424923,0.00009353373,0.01033368],"genre_scores_gemma":[0.07074777,0.0004533882,0.8973308,0.02745801,0.00345517,0.0001623403,0.0001567942,0.00001389431,0.0002218126],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8048442,"threshold_uncertainty_score":0.9980109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03410358676360445,"score_gpt":0.2678298939607005,"score_spread":0.2337263071970961,"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."}}