{"id":"W2000836571","doi":"10.6000/1929-6029.2015.04.01.10","title":"Inferential Procedures for Comparing the Accuracy and Intrinsic Measures of Multivariate Receiver Operating Characteristic (MROC) Curve","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University Grants Commission; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Receiver operating characteristic; Multivariate statistics; Sensitivity (control systems); Multivariate analysis; Measure (data warehouse); Pattern recognition (psychology); Mathematics; Artificial intelligence; Statistics; Computer science; Data mining","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005985287,0.00007505165,0.0001935358,0.0001050813,0.00006891447,0.0000727962,0.0006262444,0.00006204633,0.0001548076],"category_scores_gemma":[0.07939085,0.00004805551,0.00002237024,0.0001276729,0.0005889279,0.0001204212,0.0003675186,0.0006281545,0.000004359012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001730052,"about_ca_system_score_gemma":0.0002289215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003491244,"about_ca_topic_score_gemma":0.0002081964,"domain_scores_codex":[0.9965838,0.0003290004,0.0005696205,0.0001304512,0.002174255,0.0002128239],"domain_scores_gemma":[0.9957463,0.003216929,0.0002305976,0.00006550182,0.0005569434,0.000183797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002826538,0.001161416,0.3973381,0.0001633882,0.000322697,0.0005703462,0.01096046,0.01537233,0.007293584,0.01501749,0.009992323,0.5389813],"study_design_scores_gemma":[0.006892769,0.001430113,0.3778315,0.001412495,0.00003798703,0.0004011213,0.0005370292,0.544787,0.001097847,0.05721796,0.00799699,0.0003571267],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9691817,0.00004036778,0.02864398,0.001364116,0.0003200263,0.0001679832,0.00001908794,0.000002653621,0.0002601124],"genre_scores_gemma":[0.9925643,0.0000680429,0.007090793,0.00006975682,0.0001729481,0.000006504109,0.000004367005,0.000006934862,0.00001633641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5386242,"threshold_uncertainty_score":0.9283639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1923395717918632,"score_gpt":0.4440647863300721,"score_spread":0.2517252145382089,"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."}}