{"id":"W2745438039","doi":"10.1071/aj11092","title":"Analysis of predictive performance in the Eromanga Basin","year":2012,"lang":"en","type":"article","venue":"The APPEA Journal","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro-Canada","funders":"","keywords":"Probabilistic logic; Computer science; Optimism; Data science; Predictive modelling; Transparency (behavior); Risk analysis (engineering); Big data; Operations research; Data mining; Machine learning; Artificial intelligence; Engineering; Psychology; Business","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":[0.001656431,0.00005966546,0.0001224026,0.0001522102,0.00004638909,0.00001541261,0.0002158556,0.00002411163,0.00002886825],"category_scores_gemma":[0.00003377126,0.00003195373,0.00007579879,0.000578357,0.00001707149,0.0001255698,0.000009667485,0.0002589106,0.000002784542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002136481,"about_ca_system_score_gemma":0.000003697847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002179778,"about_ca_topic_score_gemma":7.043746e-7,"domain_scores_codex":[0.999356,0.0001152054,0.0001784706,0.00002563304,0.0001676077,0.0001570569],"domain_scores_gemma":[0.9996046,0.0001582997,0.00003193659,0.0001587144,0.00001752772,0.00002896234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004825422,0.00000750458,0.0550088,0.000005995601,0.0001439895,3.040818e-7,0.002019678,0.9417863,0.00008447708,0.00003896405,0.00006318252,0.0008359478],"study_design_scores_gemma":[0.00008663066,0.000008908017,0.4750926,0.000008725951,0.00008648928,0.00000981134,0.0001790649,0.5236199,0.0001342916,0.00001428145,0.0007276736,0.00003152274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.965641,0.0005461907,0.03173578,0.00005191817,0.0001199823,0.00003596095,0.000001430619,0.00001586116,0.001851873],"genre_scores_gemma":[0.9989928,0.0001051588,0.0007370011,0.00001353652,0.0001236293,0.000002322411,7.558252e-7,0.000006798532,0.00001798274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4200839,"threshold_uncertainty_score":0.1303035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0176069170353529,"score_gpt":0.2592655334865552,"score_spread":0.2416586164512023,"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."}}