{"id":"W2106760772","doi":"10.1109/icci.2004.2","title":"A comparison of data preprocessing strategies for neural network modeling of oil production prediction","year":2004,"lang":"en","type":"article","venue":"IEEE International Conference on Cognitive Informatics","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Data mining; Data pre-processing; Preprocessor; Artificial neural network; Computer science; Data set; Set (abstract data type); Production (economics); Data modeling; Oil production; Oil well; Artificial intelligence; Petroleum engineering; Engineering; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003617629,0.001157567,0.0008731422,0.001402135,0.0003865532,0.001107632,0.001300447,0.0007521202,0.001083249],"category_scores_gemma":[0.01178876,0.000553277,0.0009163648,0.001623684,0.0002710463,0.001953868,0.0005994073,0.001068683,0.0004495232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007418048,"about_ca_system_score_gemma":0.001163131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01138021,"about_ca_topic_score_gemma":0.01175689,"domain_scores_codex":[0.9988402,0.0005325426,0.0001168647,0.0001209883,0.0003322441,0.00005727978],"domain_scores_gemma":[0.9953306,0.003139186,0.0002000476,0.0003062718,0.0009706731,0.00005327624],"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":[0.001132263,0.0003694517,0.005139425,0.0005206579,0.0004672937,0.0001483944,0.0002404837,0.5633203,0.008887534,0.007923655,0.001618338,0.4102322],"study_design_scores_gemma":[0.00003178376,0.0001161874,0.000899647,0.00003895699,0.00006454605,0.00002509707,0.00004540741,0.9898276,0.006050426,0.001737891,0.001134119,0.0000283058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06929582,0.001079657,0.9246174,0.000424148,0.00007757967,0.0002673214,0.0003326635,0.001759818,0.00214559],"genre_scores_gemma":[0.3383522,0.001993699,0.6559097,0.0001210888,0.00004195275,0.0006947459,0.00102403,0.0003477838,0.001514776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01138021,"threshold_uncertainty_score":0.02262789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2202688620875915,"score_gpt":0.4109497479750396,"score_spread":0.1906808858874481,"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."}}