{"id":"W2992401949","doi":"10.2175/193864717822156082","title":"Using Models for Training","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Water Environment Federation","topic":"Human Resource Development and Performance Evaluation","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hydromantis Environmental Software Solutions (Canada)","funders":"","keywords":"Training (meteorology); Computer science; Geography; Meteorology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004235753,0.00008220282,0.00008811791,0.0000254534,0.0008891354,0.0001249338,0.0002037177,0.00005292029,0.0001636823],"category_scores_gemma":[0.000007875609,0.00004682969,0.00004905362,0.000004771273,0.00005285425,0.0003191505,0.00006094509,0.00004541841,0.0000132552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003768065,"about_ca_system_score_gemma":0.000004100646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000043889,"about_ca_topic_score_gemma":6.542519e-7,"domain_scores_codex":[0.9993545,0.000003869714,0.0001773628,0.0001491556,0.000155455,0.0001596809],"domain_scores_gemma":[0.9996708,0.000004651187,0.0001782483,0.0001087277,0.00002130968,0.00001623273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005062451,0.0002454868,0.03584112,0.0001939062,0.0002911201,1.721131e-7,0.1993006,0.01057435,0.6890072,0.02028101,0.001894969,0.04186374],"study_design_scores_gemma":[0.004845915,0.0002883526,0.08327708,0.0001915958,0.0002732682,0.00001349804,0.00609704,0.2989563,0.5381737,0.05138057,0.01557105,0.0009316165],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897006,0.000004846062,0.002007922,0.0005318074,0.0001914882,0.0004348567,0.000001429985,0.00000888176,0.007118191],"genre_scores_gemma":[0.9948013,0.000001819996,0.001739411,0.00003648578,0.0001255272,0.00006334771,0.000005001386,0.00001283428,0.003214228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.288382,"threshold_uncertainty_score":0.6838601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1998226792241878,"score_gpt":0.3371265082201167,"score_spread":0.1373038289959289,"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."}}